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Data-Driven ProductionINDUSTRY 4.0 BAROMETER 2025Bennet BeckerMHPEmina DelalicMHPMuriel HerfMHPProject ManagerJulius PetersMHP Christoph UngerMHPLukas MeyerMHPNiclas MaasackersMHPExpertDr.Walter HeibeyMHPAuthors ExpertDr.Oliver KelkarMHPSponsorProf.Dr.Johann KranzLMUHead of the Chair for DigitalServices and Sustainabilitykranzlmu.deSponsorDr.Christian FiebigMHPSponsorDr.Christina ReichMHP Sponsors&ExpertsContentForeword 4Summary 6 Key Findings 81.Introducing the Industry 4.0 Barometer 2025 121.1.Focus 151.2.Evaluation Method 161.3.Interviews and Success Stories 161.4.Participants 162.Results of the Survey 202.1.Status Quo of Industry 4.0 23Success Story Seamless Data Flow Between IT and OT:Digitalization with “integrate_it”and“Sounce”36Interview Schaeffler Group 402.2.Data-Driven ProductionUse of Data in Production 44Interview TRUMPF SE Co.KG 52Success Story TRUMPF Data Integration Platform 60Interview Department of Cyber and Information Technology,BMVg 642.3.Role of the CIO 69Success Story Next-Level Order-Monitoring Porsche AG 70Success Story Technology Company ZF 763.Conclusion 79Recommendations for Action 81Contact International 83Figures 8423Industry 4.0 Barometer 2025Dear Readers,In recent years,numerous global economic developments have reshaped the framework conditions for our companies and customerswith lasting effects.Many sectors are still grappling with the consequences of the COVID-19 pandemic,as well as supply chain disruptions caused by political,economic,and social instabilities and conflicts.Yet,new major challenges are already on the horizon.The future direction of the U.S.government regarding trade relations and agreements could lead to fundamental changes in global economic structures.Ongoing tensions between the worlds two largest economiesthe United States and Chinaare also intensifying,once again impacting supply chains,regulations,energy supply,and inflationparticularly in the technology sector.These economic and political developments underscore the continuous transformation of our economic environment,making a forward-looking and flexible corporate strategy more essential than ever.Last years Industry 4.0 Barometer,which focused on artificial intelligence in manufacturing,revealed a key insight:despite noticeable progress in Industry 4.0,data silos and a lack of consistent data exchange continue to hinder the adoption of further Industry 4.0 technologies.Without this foundation,the full potential of artificial intelligence remains untapped,limiting the opportunities in a data-driven economy.Building on these findings,this years Industry 4.0 Barometer is dedicated to the fundamental cornerstone of every successful digital transformation:data.With the Industry 4.0 Barometer 2025,we not only provide a comprehensive overview of the current status of Industry 4.0 activities,but also offer valuable insights from success stories and expert interviews,illustrating how companies are advancing their data strategies to prepare for a data-driven future.We therefore invite you to discover with us the important role of data as a catalyst for digital transformationand thus take a decisive step toward future resilience.Before wishing you an insightful read,I would like to extend my gratitude to Professor Dr.Johann Kranz of Ludwig Maximilian University of Munich.This marks our seventh collaboration on the Industry 4.0 Barometer.A special thanks also to the more than 800 respondents and industry experts who contributed to our study.Together,we are committed to pioneering solutions that offer a resilient response to crises and shape a digital and better future.Exactly according to our purpose:Enabling You to Shape a Better Tomorrow.I wish you all the best for 2025.Yours,Markus WambachGroup COOMHP Management-und IT-Beratung GmbHForewordIndustry 4.0 Barometer 2025 /Foreword54The Industry 4.0 Barometer 2025 provides a comprehensive overview of the current state of Industry 4.0 across various industries in 2024.Companies from the so-called DACH region(Germany,Austria,and Switzerland),the United Kingdom(U.K.),the United States(USA),and China were surveyed on their digitalization initiatives and progress.In addition to the annually considered topics of technology,IT integration,strategy and goals,as well as obstacles,this years focus is on Data-Driven Production.As in the previous year,businesses in 2024 faced significant challenges:geopolitical tensions and uncertainties,looming trade restrictions,rising energy costs,the effects of advancing climate change,the persistent shortage of specialists,and the lasting impact of the COVID-19 pandemic have all determined the economic framework.On the one hand,companies must ensure economic stability and minimize expenses,while on the other hand,they must continue to invest in innovations and digitalization to remain competitive.Despite these challenging conditions,developments in Industry 4.0 continue,particularly in the areas of automation,autonomous systems,and digital twins.The results reveal that many companies,particularly in the DACH region,have only just begun their digital transformation.An international comparison clearly shows that the U.S.and China are advancing at a significantly higher pace,thereby solidifying their leading positions in Industry 4.0.While these countries benefit from innovation-promoting regulations and targeted investments,companies in the DACH region and the U.K.continue to struggle with structural barriers.Outdated IT infrastructures,a shortage of skilled professionals,and a lack of prioritization by management remain the biggest obstacles.This is particularly evident in the automotive sector and among smaller enterprises.One of the central findings of the study is that many companies,while possessing a data foundation,do not leverage it effectively to support data-driven decision-making or drive innovation.Most companies still lack a comprehensive data strategy,which is,however,an essential prerequisite for progress in key future technologies such as artificial intelligence or digital twins.Companies in the U.S.and China are significantly more advanced in this area,using data operationally to optimize industrial processes.In contrast,companies in the DACH region face a considerable need for advancement,as the digitalization of many processes is hindered by data silos and a lack of data exchange.At the same time,limited budgets and a focus on existing production processes often complicate the implementation of modern technologies.However,medium-sized enterprises frequently emerge as pioneers,surpassing large corporations thanks to flatter hierarchies and a pragmatic approach.Furthermore,it becomes evident that foundational investments in data management,IT infrastructure,and data literacy are essential to remain competitive.Additionally,targeted training programs,collaboration with external experts,and the development of scalable IT solutions are crucial.A particularly noteworthy finding is the central role of the CIO.Companies where the CIO holds an active role in executive leadership demonstrate a 32 percent higher performance in technical data analysis capabilities.A stronger integration of CIOs into leadership roles and the consistent promotion of data-driven production processes can therefore further accelerate transformation.The successful implementation and utilization of Industry 4.0 technologies is a crucial factor in securing the economys global competitiveness.To drive this transformation forward,targeted and holistic political frameworks are required.By reducing bureaucracy and simplifying regulatory requirements,significant barriers to operationalization can be mitigated.Comprehensive skill development programs counteract the shortage of skilled professionals and capacity constraints,for example,by making STEM subjects in the education system more attractive and practice-oriented.Small and medium-sized enterprises require tax incentives and simplified access to funding to strengthen their innovative capacity.Finally,increased international collaboration is essential to benefit from best practices and reduce the technological gap with leading countries such as the U.S.and China.A synergistic approach,combining corporate initiatives and targeted political measures,is the key to seizing the potential of Industry 4.0 and ensuring long-term global competitiveness.Summary6Industry 4.0 Barometer 2025 /Summary7Key FindingsGENERAL2.Digital Twin as a growth driverlogistics as a pioneer:The Digital Twin shows the strongest development among the surveyed technologies and significantly contributes to efficiency gains and transparency.Particularly substantial progress has been made in the logistics sector.In the DACH region,however,the full potential of the digital twin remains underutilized,especially in production.1.The U.S.and China as drivers of the digital transformation in manufacturingthe gap with the DACH region widens:The DACH region faces significant challenges in data analysis capabilities and automation.A rigid focus on cost efficiency and risk minimization can hinder the flexibility and willingness to experiment that are essential for digital transformation.In contrast,China and the U.S.are increasingly adopting an agile approach that facilitates innovation.With strategic focus and an experimental corporate culture,they are breaking new ground and actively shaping the digital transition.3.Supply chain transparencyquality as the key to cost reduction:Real-time tracking and sensor technology not only increase agility and resilience but also enable early detection of quality issues,preventing high downstream costs.China serves as a front-runner,showcasing the advantages of these technologies.4.Legacy IT and data silos as innovation blockers for small enterprises:Outdated IT infrastructures and isolated data silos remain a major barrierespecially for small businesses with fewer than 100 employees.Limited investment capabilities exacerbate these challenges.5.Barriers decreaseskilled labor shortages and legacy systems remain challenges:Industry 4.0 obstacles are increasingly being overcome with regard to global development.However,skilled labor shortages and outdated systems remain significant hurdles,particularly in the DACH region.Outdated IT infrastructures and a lack of management attention are major barriers to progress.In contrast,China is successfully advancing digitalization through more flexible structures and the elimination of data silos.Industry 4.0 Barometer 2025 /Key Findings985.Data-driven economy seizing new opportunities:Data-driven decision-making holds potential for cost reduction,process efficiency,and quality improvements.However,companies in the DACH region,particularly in the automotive industry,have yet to fully leverage data as a strategic asset.Traditional structures and a limited focus on digital transformation slow down innovation and the adoption of data-driven business models,resulting in competitive disadvantages.Investments in training,infrastructure,and data-driven processes are crucial to combining quality assurance with cost reduction.Key FindingsDATA-DRIVEN PRODUCTION 1.Skilled labor shortages and lack of training as growth barriers:The skilled labor shortage in the DACH region is severely hindering digital transformation.Additionally,there is a lag in training in data literacy and data-driven decision-making.Without targeted education and training programs,companies risk falling even further behind internationally.2.Defensive data strategies dominateinnovation is neglected:While the DACH region focuses on compliance,risk minimization,and process optimization,the U.S.and China take a more aggressive approach.They leverage data to develop new services,enter new markets,and generate additional revenue.By prioritizing security,the DACH region risks losing competitiveness and missing out on growth opportunities.3.CIO as a game-changerdigitalization requires leadership:The results clearly show that an actively involved CIO not only enables more efficient IT governance but also plays a crucial role in breaking down data silos and scaling IT architecturesa key lever for the successful implementation of Industry 4.0.Particularly in the context of data-driven production,embedding the CIO role at the board level is becoming an absolute necessity.4.Data governancedecentralized vs.centralized.China as a role model:China demonstrates how decentralized approaches can foster flexibility and innovation,while the DACH region remains committed to centralized structures that hinder progress.The U.S.and the U.K.achieve better results through hybrid models.To remain competitive and drive innovation,the DACH region must adopt a more flexible approach to data governance.Industry 4.0 Barometer 2025 /Key Findings1110Introducing the Industry 4.0 Barometer 2025Industry 4.0 Barometer 2025 /Introducing the Industry 4.0 Barometer 20251312Chapter 1 defines the fundamental framework for this study.It outlines the thematic focus of this years edi-tion,supplemented by an explanation of the applied evaluation methodology.Additionally,it presents the selection of interview partners,relevant success sto-ries,and an overview of the survey participants.The goal is to establish a solid foundation for the subse-quent analyses and results.1.1.FocusThe adoption of Industry 4.0 continues to progress steadily and has already become a lived reality in many companies.The Industry 4.0 Barometer captures how this reality takes shape across industries and regions,what understanding of Industry 4.0 exists,and how mature the various technologies of the fourth indus-trial revolution are in practice.It provides valuable in-sights into existing gaps and opportunities within In-dustry 4.0.Additionally,it underscores how companies overcome these gaps,seize opportunities,and further strengthen their competitive position.To give compa-nies a comprehensive overview,MHP,in collaboration with Ludwig-Maximilians-University(LMU)Munich,has examined the status quo of Industry 4.0 imple-mentations for the seventh time.The results of this annual benchmark study offer insights into the state of Industry 4.0 activities at a given point in time across companies in the DACH region(Germany,Austria,Switzerland),the United Kingdom(U.K.),the United States(USA),and China.The questionnaire used each year covers four keytopic clusters:1.Technology:Efficient use of Industry 4.0 technologies(supply chain transparency,Digital Twin,automation,and autonomous systems)2.IT Integration:Enhancing the performance of internal IT infrastructure(data analytics and IT security)3.Strategy and Goals:Strategic focus on Industry 4.0 activities4.Obstacles:Factors with a negative impact on the implementation of Industry 4.0 technologiesEach year,current digitalization trends are incorporat-ed into the study,ensuring it remains up to date.The Industry 4.0 Barometer 2025 focuses on Data-Driven Production,deliberately emphasizing a key aspect of digitalization:data.It becomes evident that data is not a new discovery but has always been the foun-dation for innovation and modern technologies.The historical development of the Industry 4.0 Barometer under-lines this point:While previous editions focused on Industrial AI(2024)and Shopfloor Automation&Sus-tainable Operations(2023),this years focus topic builds directly on these foundationsdata.In the context of data-driven production,the prop-er handling of data is especially crucial.The effective use of high-quality and relevant data is the key to future-oriented developments in production and lo-gistics.The study defines data-driven production as an approach that,through precise data processing and interpretation,enables smart,sustainable,and resilient processes.The goal is to generate actionable insights that optimize decision-mak-ing,improve operational workflows,and in-crease added value.This data-driven approach fosters continuous innovation and efficiency,supports strategic objectives,and creates a sus-tainable competitive advantage.Digital transformation is a management task and cannot be delegated.In addition to a data strategy with a clear technological vision,this also includes the roadmap from data silos and an often outdated IT infrastructure to a continuous exchange of data(data-driven at its best).The quality of the data must have the same importance for management as other classic KPIs(e.g.the OEE).Dr.Christian FiebigPartnerDigital Factory&Supply ChainMHP Industry 4.0 Barometer 2025 /Introducing the Industry 4.0 Barometer 20251514fewer than 1,0001,000 10,00010,00024.9126.2524.4224.42Figure 2:Distribution of participants by size of businessFigure 1:Distribution of participants by region1.2.Evaluation MethodThe Industry 4.0 Barometer 2025 provides a detailed insight into the development status of companies from various industries in the field of Industry 4.0 in 2024.The study was based on a comprehensive ques-tionnaire that included various five-and seven-point Likert scales to capture participants responses in a differentiated manner.The results were categorized into topic clusters and converted into percentage val-uesso-called Barometer resultswhich were cal-culated using the weighted arithmetic mean of the responses.Additionally,participants were able to distribute 100 points across different options in prioritization questions.The original Likert scale values were trans-formed into metric scales(05 or 07)and combined with the relative frequencies of responses to calcu-late Barometer results ranging from 0 to 100 per-cent.These Barometer results serve as benchmarks,enabling comparisons of results based on different characteristics of participants and their companies.The survey was conducted anonymously to ensure valid and representative insights.1.3.Interviews and Success StoriesIn addition to analyzing the survey results,the Indus-try 4.0 Barometer also includes interviews with lead-ing industry experts,as well as MHP Success Stories that highlight the practical application of Industry 4.0 technologies.The interviewed experts not only provide insights into the focus topic of data-driven production,but also share their personal assessments of the progress of digital transformation in the in-dustry.Furthermore,they were asked about specific use cases and digitalization projects within their own organizations.The following experts were interviewed:Thomas Speck,CIO(TRUMPF SE Co.KG)Alexander B.Wurst,Vice President Process Development&Digitalization Division Bearings&Industrial Solutions(Schaeffler AG)Frank Endler,Colonel(GS)(Dept.CIT,BMVg)The MHP Success Stories present practical applications in the field of data-driven production,focusing on the implementation of data-based solutions.In addition to the initial challenges faced by companies,the pro-cess of implementing the solution and the resulting outcomes are particularly highlighted.One example is the IoT transformation in product development at ZF,where a central measurement data platform was introduced.This platform enables cross-site collection,exploration,and automated analysis of measurement data.Another example is the Data Integration Platform(DIP)at TRUMPF,which accelerates the digitalization of the innovation process and serves as a foundation for generating new value streams.The MHP Success Story“Next-Level Order-Monitoring”at Porsche de-scribes the introduction of a modern analytics and re-porting architecture to improve order management.By using an event-driven,modular architecture on AWS,which enables fast response times and real-time data consistency,the digital transformation of production processes is supported and optimized for future scal-ability.The MHP Success Story“integrate_it”presents an intelligent middleware solution developed by MHP,already in use for quality assurance at a German au-tomotive manufacturer.integrate_it not only collects,transforms,and distributes machine data,but also links sensor and machine data,paving the way for AI applications,among other use cases.1.4.ParticipantsThe results of the Industry 4.0 Barometer 2025 are based on feedback from a total of 823 participants from different regions.This includes 216 participants from the DACH region,201 from the United Kingdom(U.K.),201 from the United States(U.S.),and 205 from China(Figure 1).The analysis of company sizes among the participants presents a diverse picture:67 percent of respondents work in small and medium-sized enterprises(SMEs)with fewer than 1,000 employees,22 percent are employed in companies with 1,000 to 10,000 em-ployees,and 11 percent work in companies with more than 10,000 employees(Figure 2).The survey included participants from all hierarchical levels,from operational staff to top management.The majority of participants can be classified up to the third level below executive management.The infor-mation and communication technology sector is the most represented industry at 24 percent,followed by construction and healthcare(each at 9 percent).The automotive industry(OEMs and suppliers combined)accounts for only 4 percent of respondents(Figure 3).The most frequently represented departments are IT(30 percent)and production(13 percent)(Figure 4).This aligns with the focus areas of the Industry 4.0 Barometer.16Industry 4.0 Barometer 2025 /Introducing the Industry 4.0 Barometer 202517Figure 3:Distribution of participants by sectorFigure 4:Distribution of participants by departmentInformation&Communication Technology(ICT)Construction IndustryHealthcare&Medical SectorProductionExecutive ManagementLogisticsMarketing/HR/Other FunctionsSales and AftersalesFinanceProcurementConsumer GoodsRetail&WholesaleEnergy&Water SupplyCommunication/PR/AdvertisingFurniture IndustryOther IndustriesMechanical&Plant EngineeringTransport&LogisticsMetal Production&ProcessingElectrical&Electronics IndustryTextile&Apparel IndustryPaper&Printing IndustryPlastics IndustryAgriculture&ForestryChemical IndustryIn which industry is your company primarily active?R&DAutomotive OEM&SuppliereachIn which area of responsibility do you work?18Industry 4.0 Barometer 2025 /Introducing the Industry 4.0 Barometer 202519Results of the SurveyIndustry 4.0 Barometer 2025 /Results of the Survey2120Dr.Walter HeibeyPartnerPorsche Group|ProductionMHPAt the beginning of the chapter,an overview of the In-dustry 4.0 status quo is provided,supplemented by an analysis of the results categorized into clusters:tech-nology,IT integration,strategy and goals,as well as obstacles.Subsequently,specific findings on the focus topic of data-driven production are presented.Finally,the role of the Chief Information Officer(CIO)and its significance for Industry 4.0 activities are analyzed in greater depth.This chapter thus provides a structured foundation for interpreting and discussing the studys findings.2.1.Status Quo of Industry 4.0The global economy is caught between the poles of digital transformation,increasing connectivity and au-tomation,sustainable practices and global trade links.The term Industry 4.0 was originally coined in 2012 to describe the vision of a fourth industrial revolution driven by the integration of cyberphysical systems,the Internet of Things(IoT),and intelligent automa-tion.Today,the challenges have evolved:technologies such as Artificial Intelligence(AI),big data,and cloud computing are driving innovation,while geopolitical uncertainties and market volatility shape the broader landscape.Since 2018,the Industry 4.0 Barometer has annually examined current topics such as industrial AI,shopfloor automation,and sustainable production,reflecting the ongoing transformation.Several key trends are emerging for 2025,including the increased use of generative AI for automation,improvements in cybersecurity,and the optimization of digitized supply chains.These developments are supported by techno-logical advances such as the Internet of Things,Ma-chine Learning(ML),and cloud and edge computing.At the core of these key technologies are data and data-driven approaches.These approaches prioritize the extensive use of data to improve decision-making processes,operational procedures,and strategic ob-jectives.The United States and China are often consid-ered pioneers in this regard.After focusing on specific topics such as shopfloor au-tomation and artificial intelligence in previous years,the Industry 4.0 Barometer 2025 deliberately returns to the fundamentals:data.Our cross-industry project experience increasingly indicates that many companies face significant hurdles in implementing and applying advanced technologies.The reasons include the lack of a suitable data infrastructure,a well-thought-out data utilization strategy,or a data-driven corporate culture,which is an essential foundation for the fur-ther deployment of technologies and developments.These issues need to be examined and verified through the studys findings.Given the importance attributed to data,this years Industry 4.0 Barometer focuses on“Data-Driven Production”with the aim of providing readers with a fact-based evaluation of the topic and offering practical solutions through selected interviews and success stories.The analysis of historical Barometer results from 2022 to 2025 in the current study covers the topic clusters Technology,IT Integration,Strategy and Goals,and Obstacles.Previous studies are not considered due to a lack of comparability.In the Technology cluster,the ar-eas of supply chain transparency,digital twin,and au-tomation&autonomous systems are examined.The IT Integration Cluster focuses on IT architecture,IT secu-rity,IT system scalability,and data analytics capabilities.The Strategy and Goals Cluster presents an overview of The Obstacles Cluster investigates key resources and prerequisites for the implementation of Industry 4.0 technologies,covering both hindering factors such as a skilled labor shortage,capacity constraints,supply chain issues,and uncertainties regarding ROI,as well as enabling conditions such as the elimination of data silos and the management of legacy systems.The Total Barometer result is determined as the av-erage of the Barometer results across the three topic clusters per year.The Barometer results of the Obsta-cles Cluster have been inverted to reflect an opposite scale,thus adequately depicting progress in overcom-ing these hurdles.The results indicate positive developments in the ex-amined areas over the analysis period.From 2022 to 2025,the Total Barometer result increased significant-ly from 48 percent to 64 percent,representing a 33 percent growth(Figure 5).In the subsequent survey results,this increase is explored in greater detail within each cluster.Industrial companies will only gain a data-related competitive advantage if data is analyzed and understood organically in the context of their own production processes.Although IT is the primary technological enabler of data-driven production,data competence must be recognized as an overall entrepreneurial challenge that also affects the departments to the same extent.This applies in particular to the DACH region,which has the greatest need for action internationally.Industry 4.0 Barometer 2025 /Results of the Survey2322Figure 5:Total Barometer comparison over the yearsno deploymentdeployment plannedongoing practical testspartial deploymentfull deploymentIndustry 4.0 BarometerIndustry 4.0BarometerIndustry 4.0 BarometerDevelopment of the Technology Cluster over the YearsSupply Chain TransparencyIT ArchitectureIT SecurityScaling of IT SystemsResourcesData Analytics CapabilitiesPrerequisites Digital TwinAutomation&Autonomous SystemsDevelopment of the IT Integration Cluster over the YearsDevelopment of the Obstacles Cluster over the YearsTotal Barometer value in comparison of topic clusters over the yearsTechnological equipment along the entire value chainOur production facilities,systems,warehouse,and logistics operations are equipped with sensors to capture and transmit environmental parameters and condition data.In my company,all individual components and final products can be tracked and traced across the entire value chain.Figure 6:Technological equipment along the entire value chain*Barometer value:Weighted arithmetic mean as a percentage value*Barometer value:Weighted arithmetic mean as a percentage valueSupply Chain TransparencyThis topic cluster analyzes the implementation status of product traceability along the entire value chain,as well as the collection and transmission of environmen-tal and condition data through sensor-based systems in production,warehousing,and logistics.The precise tracking of products and resources can significantly improve efficiency and transparency in supply chains.Through real-time tracking,companies can accurately determine the location of their goods,ensure resilience in volatile markets,and respond more flexibly to disruptions and unforeseen events.Trans-parency is increasingly seen as a strategic element to enhance agility and resilience across the entire supply chain.Accordingly,companies are investing more in measures to improve supply chain transparency.Topic Cluster 1:TechnologyIn the survey,60 percent of participants state that their company is partially or fully capable of tracking both individual components and end products.This represents an improvement of six percentage points compared to the previous year.The Barometer result further highlights this progress:it increased from 49 percent(2023)to 60 percent(2024)and reached 64 percent in the current survey.This represents the high-est value within the examined Technology Cluster.A similar trend is observed in the field of sensor technol-ogy.Here,the Barometer result rose from 44 percent(2023)to 53 percent(2024)and ultimately to 60 per-cent in 2025(Figure 6).24Industry 4.0 Barometer 2025 /Results of the Survey25no deploymentdeployment plannedongoing practical testspartial deploymentfull deploymentno deploymentdeployment plannedongoing practical testspartial deploymentfull deploymentFigure 8:Deployment of sensor technology in production,warehousing,and logistics by regionFigure 7:Supply chain transparency by regionIn my company,all individual components and final products can be tracked and traced across the entire value chain(from inbound logistics to production to customer service)(Traceability).Our production facilities,systems,warehouse,and logistics operations are equipped with sensors to capture and transmit environmental parameters and condition data.In international comparison,China leads in the use of tracking technologies.71 percent of respondents there indicate that their company is partially or ful-ly capable of tracking individual components or end products.In the DACH region,this figure is only 47 percent,while it is 59 percent in the U.S.and 65 per-cent in the U.K.(Figure 7).Notably,the proportion of companies without tracking technologies is highest in the DACH region at 18 percent,whereas nearly all respondents in China state that they at least have im-plementation plans.China is also ahead in the field of sensor technology:65 percent of participating com-panies partially or fully capture and transmit environ-mental parameters and condition data.In the DACH region,this figure stands at 44 percent,compared to 57 percent in the U.S.and 52 percent in the U.K.A significant gap is evident in the DACH region:22 per-cent of surveyed companies do not comprehensively use production facility sensors to collect and transmit environmental parameters and condition data across production,products,and logistics(Figure 8).By con-trast,all surveyed Chinese companies report using sensorsclearly highlighting the significant gap and the urgent need for the DACH region to catch up.26Industry 4.0 Barometer 2025 /Results of the Survey27no deploymentno deploymentdeployment planneddeployment plannedongoing practical testsongoing practical testspartial deploymentpartial deploymentfull deploymentfull deploymentIndustry 4.0 BarometerIndustry 4.0 BarometerIndustry 4.0 BarometerFigure 9:Distribution of the Digital TwinFigure 10:Distribution of the Digital Twin by regionIn my company,there is a digital replica that captures process and condition data for simulation,control,and optimization ofour products.our entire logistics operations.our production facilities.In my company,there is a digital replica that captures process and condition data for simulation,control,and optimization of our production facilities.*Barometer value:Weighted arithmetic mean as a percentage valueDigital TwinThe increasing willingness of companies to invest more heavily in the digitalization of their supply chains is primarily reflected in the increased use of digital twins for simulation,control,and optimi-zation.This trend is also evident in the rise of the Barometer result for product usage,which increases from 51 percent in 2024 to 58 percent in 2025.The use of digital twins in production facilities increased from 48 percent(2024)to 54 percent(2025),and in logistics from 52 percent(2024)to 61 percent(2025)(Figure 9).The intensified use of digital twins,partic-ularly in logistics,can likely be explained by the fact that logistics plays a central role in maintaining pro-duction processes and overall operational continuity.Implementing digital twins enhances transparency in the supply chain,leading to more efficient operations and better control of logistics processes.Consequent-ly,companies currently perceive the greatest benefits of digital twins in logistics.The high acceptance and increasingly prioritized use of these technologies in-dicate that companies are recognizing and leveraging the value digital twins offerparticularly in terms of transparency,flexibility,and efficiency improvements.Despite widespread use in logistics,the potential of this technology,especially in the optimization of pro-duction and production facilities,has not yet been fully realized.An international comparison shows that China is leading in this field:67 percent of participating Chi-nese companies use digital replicas in their produc-tion facilities either partially or fully.In contrast,the share in the German-speaking DACH region is only 41 percent.This difference becomes even more pro-nounced when looking at companies that do not use digital replicas at all:In the DACH region,30 percent of respondents have not yet adopted these technologies,the highest percentage compared to other regions.The U.S.and the U.K.have better val-ues,with 18 percent and 25 percent,respectively.In China,however,only 5 percent of companies do not use digital replicas(Figure 10).The described trends in the use of digital twins in production facilities are also reflected in other areas,such as product optimi-zation and logistics.The comprehensive application of the digital twin in China suggests a holistic digi-talization of the industry,whereas the DACH region still has room for improvement in systematically de-ploying this technology.This discrepancy can largely be attributed to differing digitalization strategies and government initiatives,with China more actively pro-moting the adoption of new technologies.28Industry 4.0 Barometer 2025 /Results of the Survey29no deploymentdeployment plannedongoing practical testspartial deploymentfull deploymentno deploymentdeployment plannedongoing practical testspartial deploymentfull deploymentIndustry 4.0 BarometerIndustry 4.0 BarometerFigure 11:Maturity level of automation and autonomous systemsFigure 12:Maturity level of automation and autonomous systems by regionMaturity level of automation and autonomous systemsWe use machines and robots that can act autonomously,self-regulate,or improve themselves.Our facilities,devices,and systems exchange information autonomously and in real time(M2M).*Barometer value:Weighted arithmetic mean as a percentage valueOur facilities,devices,and systems automatically and autonomously exchange information in real time(Machine-to-Machine Communication).We use machines and robots that can act autonomously,self-regulate,or improve themselves(e.g.,autonomous transport systems).Automation and Autonomous Systems The use of automation and autonomous systems is slightly below the level of supply chain transparen-cy and digital twins(Figure 11).This lower frequency of adoption could indicate that companies are more hesitant to implement automation solutions than technologies like the digital twin,which primarily en-able real-time transparency and control.A possible reason for this is the more complex implementation processes and higher initial investments associated with introducing fully automated and autonomous systems.These systems not only re-quire technolog-ical adjustments but also necessitate a deep restruc-turing of operational processes,which slows down the adoption speed.Uncertainties regarding long-term profitability and technical integration may also lead companies to proceed cautiously in this area.The international comparison shows that China is leading in machine-to-machine communication(M2M):71 percent of surveyed Chinese companies report that their systems exchange information in real-time and automatically,whereas this figure is only 56 percent in the DACH region.The rise in the Barometer result from 57 to 63 percent indicates growing global interest and increased investments in real-time machine communication.The Chinese government views technological prog-ress as the primary driver of economic development.By promoting digitalization through reduced regu-lations,companies can adopt and implement new technologies more quickly.In the DACH region,16 percent of surveyed companies report that they do not use systems with real-time information ex-change,while this share is only 1 percent in China(Figure 12).The lower prevalence of automated and autonomous systems in the DACH region can be at-tributed to several factors.First,stricter regulatory requirements and safety standards hinder faster im-plementation.Second,cultural differences also play a role:companies in the DACH region tend to be more risk-averse and rely on long-established technologies.This also explains the slow adoption of automated guided vehicle(AGV)systems,which only 35 percent of respondents from the DACH region report using partially or fully,compared to 59 percent in China.Overall,the development of AGV system usage is positive.The Barometer result increases from 38 per-cent in 2023 to 46 percent in 2024 and reaches 50 percent in the current observation period.The reluc-tance in the DACH region to introduce AGV systems may also be linked to concerns about job losses and the complex organizational changes that such tech-nology entails.Limited financial resources and the sometimes long amortization periods of autonomous systems present significant barriers,particularly for small and medi-um-sized enterprises,preventing them from investing in this area.30Industry 4.0 Barometer 2025 /Results of the Survey31much worsemuch worseworseworsesamesamebetterbettermuch bettermuch bettermuch better/bettermuch better/bettermuch better/bettermuch better/betterIndustry 4.0 BarometerIndustry 4.0 BarometerIndustry 4.0 BarometerIndustry 4.0 BarometerFigure 13:Maturity level of data analytics capabilities*Barometer value:Weighted arithmetic mean as a percentage valueFigure 14:Data analytics capabilities by regionPlease assess your companys data analytics capabilities compared to your direct competitors,with regard tothe skills and competencies of your personnel in advanced data analytics methods.production processes with semi-and fully automated decisions.the technical infrastructure for advanced data analytics.the systematic and continuous collection,processing,and analysis of data along the entire value chain.Please assess your companys data analytics capabilities compared to your direct competitors,with regard to the skills and competencies of your personnel in advanced data analytics methods.Data Analytics CapabilitiesCompanies are increasingly recognizing that a da-ta-driven approach across the entire value chain plays a crucial role in optimizing their processes.However,de-spite significant progress in various areas of Industry 4.0 in recent years,this years results indicate that progress in data analytics capabilities is significantly lower com-pared to the previous year.As a logical consequence,Topic Cluster 2:IT Integrationthis years focus is on“Data-Driven Production.”The question arises as to how companies can utilize their data assets more effectively along the value chain to optimize and sustain their production.The greatest progress in data analytics capabilities has been observed in production processes involving par-tially and fully automated decisions,such as through the use of artificial intelligence and machine learning methods.While the Barometer result was still at 62 percent in 2024,this years study shows a rise to 66 percent,indicating the growing implementation of such technologies in production processes(Figure 13).De-spite this increase,this area still has the lowest maturity level within the field of data analytics capabilities and is rated most critically compared to other areas,under-scoring a continued need for action.The continuous expansion of technical infrastructure and the targeted development of relevant data analyt-ics capabilities have enabled companies in recent years to establish the foundations necessary to systematical-ly leverage the benefits of automated decision-making processes.Looking at overall progress since the begin-ning of the survey,companies have seen the greatest increase in this area(Figure 13).The Barometer result in the first survey in 2022 was only 36 percent.This underscores the central importance that automated decision-making processes now have in modern value creation.A closer look at the results of an international com-parison reveals significant regional differences.Com-panies were asked to assess their data analytics capa-bilities relative to their competition.The DACH region,in particular,significantly lags behind.While 78 per-cent of participants in the U.S.rate their capabilities as superior to their competitors,this is only true for 61 percent in the DACH region(Figure 14).Reasons for this include lower investments in training programs and a delayed implementation of advanced analytical methods.A targeted expansion of data capabilities is therefore essential to close the international gap and secure future competitive advantages.IT SecurityIn recent years,a consistent trend has emerged:IT security is firmly embedded within companies and treated as a priority.Once again,organizations have made progress in all areas of IT security,particularly in implementing uniform authorization concepts for company data this year.In this area,the maturity lev-el has increased from 76 to 80 percentthe highest score among all surveyed IT security topics.A total of 86 percent of respondents indicate that they have a uniform authorization framework in place.In interna-tional comparison,the DACH region and the U.K.hold leading positions,although all regions demonstrate high levels of maturity.Within the context of this years focus topic,data-driven production,access to compa-ny data is especially critical.Only through structured 32Industry 4.0 Barometer 2025 /Results of the Survey33strongly disagreedisagreesomewhat disagreeneutralsomewhat agreeagreestrongly agreeOpening up new market and customer segmentsEnhancing the flexibility of our productionImproving the quality of production and productsIncreasing cost efficiencyDevelopment of new services for existing products23Figure 15:Status of IT security by regionFigure 16:Strategic Industry 4.0 focus of the surveyed companiesParticipants could allocate a total of 100 points.The results shown here represent the average score for each response option.Access to company data is regulated by a uniform authorization concept.Strategic Industry 4.0 focus of the surveyed companiesaccess management can stakeholders retrieve the nec-essary data to make informed decisions and control automation processes.In the other two areas of IT securityassigning high importance to IT security management within the company and possessing comprehensive and suffi-cient expertise to defend against cyberattacksthe U.K.and the U.S.lead the rankings,followed by China,while the DACH region lags behind.The re-gional differences are particularly evident in terms of cyberattack defense:while 90 percent of surveyed companies in the United States agree that they have sufficient expertise,this figure is only 76 percent in the DACH region(Figure 15).As part of the Strategy and Goals Cluster,respondents were asked to distribute 100 points across five stra-tegic objectives of their companies.The results show that increasing profitability is considered the most important goal of Industry 4.0,receiving an average of 30 points.This highlights that cost efficiency and profitability are central motivations.In second place is improving product and production quality,which re-ceives an average of 23 points,emphasizing that qual-ity assurance and enhancement are also key drivers.Ranked closely together are the development of new services(17 points)and the adaptation of flexibility(16 points).At the lower end of the priority list is expan-sion into new market and customer segments,which receives an average of 14 points(Figure 16).A regional comparison reveals significant differences in the prioritization of goals.In the DACH region,in-Topic Cluster 3:Strategy and Goalscreasing profitability is particularly emphasized,receiv-ing 34 out of 100 points,while this value is only 25 points in China.A possible reason for this could be the higher labor costs in the DACH region compared to China.As a result,a stronger focus on cost efficiency is necessary to remain competitive.Companies in the DACH region face greater pressure to reduce opera-tional costs and achieve efficiency improvements in order to secure their margins.It is particularly noteworthy that the information and communication technology industry prioritizes prof-itability as the top strategic goal in all the regions consideredDACH,the USA,the U.K.,and China.In this industry,in-creasing profitability is ten times more important than it is for automobile manufacturers(OEMs).34Industry 4.0 Barometer 2025 /Results of the Survey35“Sounce”software in actionSuccess Story Seamless Data Flow Between IT and OT:Digitalization with “integrate_it”and “Sounce”Project DescriptionThe middleware solution“integrate_it”enables the capture,transformation,and distribution of machine data.This functionality not only ensures optimal control of industrial equipment but also facilitates a seamless data flow between IT and OT(Operational Technology,encompassing hardware and software for controlling industrial equipment).This compre-hensive data integration paves the way for additional applications from the MHP Industrial Cloud Solutions(ICS)portfolio,such as the AI-based anomaly detec-tion software“Sounce.”This represents a prime ex-ample of Industry 4.0 technologies that play a critical role in connectivity,standardization,andspecifical-ly in this use casequality assurance.Initial Situation and ChallengesCompanies and production managers are aware of the benefits of digital processesreal-time insights from the shop floor improve machine availability and performance,enhance manufacturing quality,and boost productivity.However,businesses often fail to ensure the necessary connectivity.The primary obsta-cle lies in heterogeneous shop floor environments.Assets(including tools,machines,and equipment)vary widely in age and manufacturer,each employ-ing a range of technologies and proprietary proto-cols.Also,a binding standard for data exchange is lacking,as is support for common machine and IT protocols.And finally,establishing cloud connectivity remains challenging.Approach and FunctionalityA German automotive manufacturer was faced with a highly heterogeneous IT and OT environment while striving for improved data integration.To enable seamless digitalization of the shop floor,MHP imple-mented the industrial cloud solution“integrate_it”as part of an ongoing project.Serving as middleware,the solution facilitated data exchange across com-mon proprietary and open protocols.This enabled the capturing of data from nearly all machines and equipment,transforming it as needed and transmit-ting it in the appropriate protocol to other shop floor assets as well as downstream IT and OT systems.A key success factor is that“integrate_it”supports common machine protocols such as OPC UA,RFC 1006,and others,as well as widely used IT proto-cols like Kafka,MQTT,and REST.Additionally,this SaaS solution can be directly connected to Amazon Web Services(AWS),Microsoft Azure,or any private cloud of the user company.A further benefit is that the solution converts captured machine data into the human-readable JSON(JavaScript Object Notation)format,enriches it,and maps it to freely configur-able data models.A rule engine enables initial rule-based manipulation of the data,allowing for imme-diate insights to inform latency-critical decisions.Results and OutlookA notable impact of“integrate_it”at the automo-tive manufacturers plant will be the consolidation of machine and sensor data to generate precise insights and predictionsparticularly in Quality Assurance.The industrial cloud solution“Sounce”has been in use in this area since 2021.With its AI-based capabilities,the solution enables the manufacturer to detect anomalies in components and products,respond rapidly,and optimize manu-facturing and assembly processes.In the quality as-surance setup,a minimally invasive acoustic sensor captures sound within test objects.The“Sounce”software converts the sound into spectrograms and compares those to stored pattern data.Deviations are immediately detected and interpreted.Deeper insights will become possible once sensor data is linked with machine data through“integrate_it.”This will allow inspectors not only to be alerted to automatically detected quality deviations but also to identify the root causes in the manufacturing machines and equipment.A significant benefit of“Sounce”is that a deep learning algorithm supports numerous additional application scenarios.More-over,fault detection enables early identification of issues even during development processes.Sounce,integrate_it,bolt_it,paint_it,supply_it,shift_it,FleetExecuter:MHP has been offering proprietary Software-as-a-Service solutions since 2023.These industrial cloud solutions incorporate the extensive experience and comprehensive expertise of MHP and of its technology and industry partners.More on this:https:/ 4.0 Barometer 2025 /Results of the Survey37strongly agreeagreesomewhat agreehistorically grown legacysystems.integration into day-to-day operations.data silos.uncertainty in scaling.supply chain issues.uncertain ROI.lack of data exchange with partners.the shortage of skilled labor.insufficient attention from management.Figure 17:Obstacles to the implementation of Industry 4.0 technologiesFigure 18:Obstacles to the implementation of Industry 4.0 technologies by regionThe implementation of Industry 4.0 technologies is delayed in our company due toThe introduction of Industry 4.0 technologies is delayed in our company because data silos hinder the implementation of cross-functional solutions.The introduction of Industry 4.0 technologies is delayed in our company because there is no seamless data exchange with partners along the value chain.strongly disagreedisagreesomewhat disagreeneutralsomewhat agreeagreestrongly agreeIn the current survey,the skilled labor shortage shows the greatest decrease.While in 2024,52 percent of participants reported that difficulties in recruiting qual-ified employees delayed the implementation of Indus-try 4.0 technologies,this figure drops to 41 percent in 2025(Figure 17).This could be attributed to targeted training initiatives,improved recruitment strategies,and the increased use of automation technologies,as well as a change in the composition of the surveyed companies.Topic Cluster 4:ObstaclesThe impact of supply chain issues as an obstacle to the implementation of Industry 4.0 has also decreased significantly.Only 31 percent of respondents cite this as a hurdle,representing a decrease of 10 percentage points compared to the previous year.This suggests that companies have developed more resilient sup-ply chains to better respond to recurring crises.The increasing adoption of resilience strategies,such as supplier diversification and the use of digital tools for real-time supply chain monitoring,is likely playing a key role here.Despite these positive developments,the integra-tion into existing legacy systems remains the biggest challenge.Once again this year,around 48 percent of respondents cite outdated IT infrastructures as the main reason for delays in the adoption of Industry 4.0 technologies.This issue is particularly relevant in the DACH region,high-lighting a structural challenge that is more common in historically developed companies,particularly in industrial sectors.Industry comparisons show that legacy systems are a central obstacle,es-pecially in the automotive industry,which has tradi-tionally been heavily dependent on long-established IT systems.The effort and complexity of modernizing outdated systems could significantly slow down prog-ress in this sector.In addition to technical hurdles,the lack of attention to Industry 4.0 at the management level has also been identified as a significant barrier.43 percent of respondents state that strategic prioriti-zation within the company is insufficient to drive the necessary investments and changes.A noteworthy de-velopment is the reduction of data silos in China.They are increasingly perceived as less significant obstacles.The share of Chinese respondents citing this challenge has dropped from 57 percent in the previous year to 41 percent in the current study(Figure 18).This trend suggests improved data integration and accelerat-ed digitalization of processes.While China is setting new standards in digitalization,the DACH region is still struggling with outdated systems.The key lies not only in technology but also in the ability to forge new paths.38Industry 4.0 Barometer 2025 /Results of the Survey39Alexander B.Wurst DTO and Digital Ambassador(Schaeffler)Schaeffler Group Schaeffler Brief ProfileSchaeffler Group We pioneer motionFor over 75 years,the Schaeffler Group has been driving groundbreaking innovations and develop-ments in motion engineering.With cutting-edge technologies,products,and services in the fields of electromobility,CO2-efficient powertrains,chassis solutions,and renewable energy,the company is a trusted partner in making movement more efficient,intelligent,and sustainableacross the entire lifecy-cle.Schaeffler presents its comprehensive product and service portfolio within the mobility ecosystem through eight product families,ranging from bearing solutions and linear guides of all kinds to repair and monitoring services.With approximately 120,000 employees at more than 250 locations in 55 coun-tries,Schaeffler is one of the worlds largest family-owned enterprises and ranks among Germanys most innovative companies.Alexander B.Wurst Brief BiographyAlexander B.Wurst is a passionate mechanical en-gineer with extensive expertise in production,dig-italization,and process optimization.As Process Development&Digitalization Vice President at the Division for Bearings&Industrial Solutions at Schaef-fler Group,he has successfully implemented an end-to-end digitalization strategy.At Schaeffler,he drives comprehensive restructuring processes,enhances efficiency,and optimizes cost structures.Recognized as a true leader and digital ambassador,he actively promotes market-oriented business models and in-novations in the field of Industry 4.0.Participants:Alexander B.Wurst(DTO and Digi-tal Ambassador at Schaeffler),Thomas Kle(MHP Senior Manager Digital Factory and Supply Chain),Tobias Schreiber(MHP Senior Consultant Digital Factory and Supply Chain)Thomas Kle:Can you give us a brief overview of your role at Schaeffler and your experience in the field of digitalization and IT thus far?Alexander B.Wurst:As part of the Division for Bearings&Industrial Solutions,we set the strategic framework for our industrial business.Our portfolio includes rolling bearing solutions,linear solutions,condition monitoring solutions for bearings,and in-novative products in the field of robotics.Within this division,I serve as the Digital Transformation Owner(DTO)and am responsible for digitalization.My team and I focus on strategic alignment,developing the roadmap,and ensuring its global implementation in collaboration with our four regions.At its core,our role is to define the direction and establish a struc-tured,coordinated approach.This involves leverag-ing synergies and aligning digitalization efforts with business objectives and divisional goals in a targeted manner.Additionally,I am responsible for Process Development.To ensure optimal alignment between processes and digitalization,we have consolidated the two within a single organizational unit.This al-lows us to create digital yet lean,stable,and robust processes while driving a major transformation in the ERP landscape.Another key aspect of my role is over-seeing the divisions IT portfolio.Together with my team,I determine which IT systems,applications,and tools will be used across our regions.The focus here is on scaling existing solutions,establishing standards,and eliminating redundancy to optimize resource ef-ficiency and ensure a more targeted approach.Thomas Kle:In your perspective,how relevant is data in 2025?Alexander B.Wurst:In my opinion,data holds im-mense relevance.At the end of the day,data is one of the production factors,alongside capital,labor,and land.Companies that manage to use data better will have a competitive advantage.Its not just about owning data,but about having the right data availa-ble in sufficient quality,as well as having the appro-priate control processes for data management.And if we can achieve all of this as a company,then we are ready to apply AI,automate processes correctly,and trust that automation.Then a company is capa-ble of providing advanced and reliable forecasts.But this is only possible if companies carefully manage,maintain,and control their data.For me,data is not only the key foundation,but also an indispensable prerequisite for improving processes and gaining an advantage in global competition.Thomas Kle:What essential prerequisites,in your opinion,still need to be met in order to successfully implement profitable data-driven production?Alexander B.Wurst:The relevance of data is strong-ly linked to the mindset of the individuals involved.Its not about the technical prerequisites needed to work with data perfectly or better.The real question is how we handle the data.Do I understand the rel-evance of data?Do I see data as the differentiating factor?If we realize that our daily interactions with a system enable a use case or bring efficiency to anoth-er part of the organization,then weve succeeded.Simply put,if fields are just being filled in for the sake of filling them,it does nothing for the company.Tobias Schreiber:When you think of the deci-sion-making processes at Schaeffler:To what extent can you rely on meaningful and valid data,and where do you still see challenges or potential for improve-ment in the data foundation for decisions?Alexander B.Wurst:For top management level de-cision-making and reporting,we are already using automated and interactive reports.For example,we were able to reduce the process duration for creating quotes for standard products to less than one day by using curated and cleansed data.However,the optimization doesnt stop there;it is an iterative process.After all,we dont just want to digitize the current state;thats merely a milestone on the way.Additionally,there is an ongoing influx of data that we must aggregate for even better de-cision-making.For instance,when creating quotes,the responsible employees dont just want to know the price for a standard product at a batch size of 100 units.They also want to know which subregion the quote is being created for,which phase of the market cycle is currently prevailing,what the stock level is,and whether its a buyers or sellers market.Market conditions are becoming more demanding,and the half-life of information is becoming shorter.Therefore,we must react more frequently,and much more quickly,to be able to prepare and support the right decisions.Thomas Kle:What long-term goals does Schaef-fler pursue in the field of digital production,and how are these goals to be achieved?What role does the“Production Traceability”initiative,in which MHP has collaborated with Schaeffler,play in the overall pic-ture,and where do you currently stand in this trans-formation process?Alexander B.Wurst:Our product portfolio is basi-cally divided into two areas:our core business,which includes both the rolling bearing and linear business,and our growth business,with which we are invest-ing into the future.This includes mechatronic prod-ucts such as robotics,condition monitoring solutions,and actuators.The Production Traceability initiative primarily targets our core business,with a particular focus on the rolling bearing business,as these prod-Interview40Industry 4.0 Barometer 2025 /Results of the Survey41ucts will remain mechanical for the majority of appli-cations.Some of this is due to space constraints,but there are also environmental factors which preclude a mechanically excellent product being further“sen-sorized”or“mechatronized.”However,we can sig-nificantly expand and enhance our core business with digital services,making it easier for the customer to digitally integrate our products into their processes.With the help of digitalization,we can also achieve significant improvements along the production chain.This is the internal dimension of the Production Traceability initiative:collecting data from the manu-facturing steps,correlating them,and not optimizing individual machines or individual production steps,but considering the entire chain,so that interactions become visible and the optimum progression along the value chain can be identified.Our product portfolio is complex and includes various manufacturing equipment and value-adding stages,some of which involve partners and suppliers.The Production Traceability initiative aims to manage this complexity and translate it into productivity improve-ments.The goal is to optimize manufacturing with the data we collect and make available within this initiative,and to provide digital services that offer the customer unmatched added value.Even when a dis-ruption or complaint occurs,we want to be able to provide meaningful insights and showcase how the product was manufactured,highlighting correlations so that the problem does not recur in the future.At Schaeffler,we have the vision of making manufactur-ing automated,autonomous,flexible,and sustaina-ble for the future.For all four dimensions,reliable data is needed to help run a machine automatically.Additionally,these are all topics that the Production Traceability initiative contributes to.Tobias Schreiber:According to our research,com-panies from the USA and China are considered to be more“experimental,”for example in terms of the acceptance of a failure culture in the area of innova-tionshow do you evaluate this aspect?Alexander B.Wurst:I believe that it is the inter-play in a global initiative that brings out the individual strengths of our international setup.I wouldnt clas-sify one region as more“experimental”than anoth-er.Viewing complex challenges from different tech-nical and cultural perspectives is a strength.Thats why,from the very beginning,we didnt limit our initiative to the DACH region,which in many cases would seem more convenient and tangible,but set it up globally.We involve our plants in China,Amer-ica,and emerging countries like India,so its not a“headquarter ivory tower initiative”that cant be implemented in practice.The initiative aims to be a blueprint and modularly adaptable at different levels,and applicable in all our plants.We also take region-al preferences into account.In China,for example,there is a stronger mobile approach,and with Aliba-ba Cloud or the WeChat ecosystem,the IT infrastruc-ture conditions are completely different.However,the key is to strike the right balance.We are a premi-um manufacturer delivering excellent products with a very high level of quality to provide our customers with durable and application-specific products.We hold the same standard for our digital solutions.And we can only meet this standard if our data structure is excellent in advance.Tobias Schreiber:What advice would you give to other companies that want to embark on the path to data-driven production?Alexander B.Wurst:As every company is some-what unique,it is never just about the IT perspec-tive when it comes to data,but always about the product,the business model,the respective market,and,above all,the specific corporate culture.Nev-ertheless,I would always recommend incorporating business relevance from the very beginning.In our initiative,we focused on the data we need for our divisional objectives.Which data supports profitable growth,the strategic expansion of the product port-folio,or customer added value?This consistency is the longest-lasting common thread in any business field.We did not accumulate the data for the sake of IT systems alone;the purpose and goals of the com-pany are clearly customer-oriented.A second piece of advice could be“embrace the gaps.”Unlike what was assumed by many a few years ago,it has been shown that it is not neces-sary to collect all data.If you have 20 out of 100 data points and they are the right ones and of high quality,then youve done the job 100%right,even if 80 data objects were not captured,or not captured digitally.One should trust that they have understood their business properly and know which data is ac-tually needed and is relevant both internally and for the customer.Thomas Kle:What trends or developments in the field of data do you consider particularly relevant,and what goals has Schaeffler set for the future?Specifically,we are referring to the Catena-X project of the DLR,in which Schaeffler is involved.Are there any concrete ideas for promoting this platform con-cept with suppliers?Alexander B.Wurst:Business relationships will like-ly become even more intertwined,with companies interacting much more closely,and also being more connected in terms of systems than is currently the case.In the exchange of information and communi-cation,for example,via emails,the human compo-nent will recede into the background and be replaced by system-to-system connections.For companies,this means they must know and master their own business processes and the associated data,so they are capable of keeping up with partners,customers,and suppliers in the increasingly complex networks,while also fulfilling regulations regarding climate and environmental protection more efficiently.Moreover,in my opinion,there is a global trend in which process excellence and increased productivity can improve the efficiency of areas of manufacturing.Here,digitalization is the key component to making production more automated and autonomous.And even with the major issues of our timesustainabil-ity,both from a resource and control perspectivedigitalization is the key.Many companies start with a central department that is supposed to turn the company inside out and of-fer only digital business processes and everything“as a service.”In a second and third wave,it becomes clear that a much more federalized approach is nec-essary,meaning that a strong IT backbone must be in place and the digitalization units must simultaneous-ly be close to the business.What is highly relevant for the customer and ultimately decisive in the market is when we,for example,take the capacity information from our manufacturing and enrich it with market data and sales information,so that our salespeople can be even more insightful and respond on a case-by-case basis.With this networking of individual(data)silos,a tangible added value is created for the customer;however,this networking also presents a challenge.We have already proven,though,that we are capable of mastering it.Regarding Catena-X:We are an integral part and partner of Catena-X,and we are shaping the ex-change of data across company boundaries to ensure that businesses maintain control over their data.We also transfer our experiences to other business areas,although outside the automotive sector the supplier and customer structures are much more heteroge-neous.Smaller suppliers often lack the resources to become part of such an ecosystem,which leads us to carefully consider when and in which use case we participate.Despite these challenges,we are techno-logically prepared and closely involved in the devel-opments.42Industry 4.0 Barometer 2025 /Results of the Survey43treats data as a strategic asset to generate valuable information across the organization.has a clear roadmap outlining when which business cases are to be solved with the help of defined and subsequently available data.has a clear plan for how data is managed to ensure high data quality and availability.strongly disagreedisagreesomewhat disagreeneutralsomewhat agreeagreestrongly agreeFigure 19:General data strategies in companiesMy company2.2.Data-Driven Production Use of Data in ProductionIn Industry 4.0,an increasing amount of data is being generated with ever finer granularity.The targeted use of these data assets within a company helps to optimize and automate,as well as to plan produc-tion processes.However,before companies can ful-ly leverage the productivity potential of data-driven production,they must systematically and purpose-fully manage the fundamental resourcedata.Data is the foundation upon which technologies such as Artificial Intelligence,Machine Learning,and the In-ternet of Things(IoT)are built.Companies that approach the topic of data compre-hensivelyconsidering requirements,usage,and ap-plicationface a variety of technical,organizational,operational,and strategic questions.We are examin-ing these in detail this year,as our experience shows that many companies still have room for improve-ment in these areas.A key aspect is interoperability,as companies often work with a variety of different systems and tech-nologies that do not always communicate seamlessly with one another.Standardized interfaces and uni-form protocols are needed to overcome these barri-ers.Companies face the challenge of not only cap-turing large amounts of data but also analyzing and utilizing it effectively.This results in challenges such as integrating heterogeneous systems,ensuring data quality,and managing security and privacy issues.Another obstacle is converting the collected data into actionable insights.Advanced analytical tools and technologies,such as machine Learning,are cru-cial in this regard to recognize patterns and enable informed decision-making.The ability to generate strategic value from data is increasingly becoming a competitive advantage.However,successful data utilization requires not only the technical means for data collection but also the development of a clear data strategy that covers the entire data lifecyclefrom collection to processing and analytics.Given the growing volumes of data and changing business requirements,it is crucial to design this strategy in a flexible and forward-looking manner.Data-driven companies cannot only increase their efficiency but also enhance their flexibility and respond more quickly to disruptions or fluctuations.The ability to strategically and securely deploy data thus becomes a critical success factor in global com-petition.Data StrategyA mature data strategy helps companies to use data strategically as a valuable resource.It cannot only pro-mote more informed decision-making and optimize business processes but also strengthen innovation and ultimately improve a companys competitiveness.The high importance of data and necessary data manage-ment was defined by the British mathematician Clive Humby back in 2006 with his comparison“Data is the new oil.”1 The true value of data,just like oil,only becomes apparent through its processing and analysis.Overall,it shows that a clear majority of the surveyed companies pursue a data strategy in relation to da-ta-driven production:76 percent have implemented a clear data strategy and corresponding measures to ensure high data quality and availability.Data strate-gies have become a central element of business de-velopment for many companies(Figure 19).Only a very small percentage of respondents still see gaps or missing strategies in this area.Additionally,74 percent of companies have developed concrete roadmaps for utilizing data,highlighting that the potential of data for optimizing business processes is already recognized in many companies.Companies have adopted a clear and positive stance on the strategic use of data.In fact,80 percent of the respondents treat data as valu-able assets(resources)for generating insights within their organizations.In terms of overall data strategies,the development of roadmaps,and the significance of data,the DACH region shows notable room for improvement in an international context.In the USA,the U.K.,and China,the implementation of clearly defined data strategies is more advanced(Figure 20).The targeted use of data and its high importance are more strongly considered and hold greater relevance.1 Humby,C.(2006).Data is the new oil!Keynote.ANA Senior Marketers Summit,Kellogg School,Evanston,IL,United States.In general,a distinction can be made between a de-fensive and an offensive data strategy.A defensive data strategy focuses on ensuring the security and protection of data.The primary emphasis is on mea-sures to minimize risks,such as data breaches and unauthorized access.The goal is to reduce potential threats and maintain data integrity.Participants were asked to what extent defensive data strategies help achieve key business goals such as reducing operat-ing costs,meeting regulatory requirements,or im-proving data quality.Overall,the results show that most companies effectively implement the defensive components of their data strategy(Figure 21).44Industry 4.0 Barometer 2025 /Results of the Survey45strongly disagreestrongly disagreedisagreedisagreesomewhat disagreesomewhat disagreeneutralneutralsomewhat agreesomewhat agreeagreeagreestrongly agreestrongly agreeFigure 20:International comparison of general data strategyFigure 21:Corporate defensive data strategyMy company has a clear plan for how data is managed to ensure high data quality and availability.My company has a clear roadmap outlining when which business cases are to be solved with the help of defined and subsequently available data.My company treats data as a strategic asset to generate valuable information across the organization.My companys defensive data strategy enhances our ability to.reduce operating costs.prevent cyberattacks and data ply with the legal requirements.reduce operational risks.optimize business processes.improve data quality.generating additional revenue,creating new products and services,or responding more quickly to market changes.Overall,the elements of an offensive data strategy are largely viewed positively.75 percent of participants generate additional revenue through ac-tive data use with existing products and services,high-lighting the economic benefits of an offensive data strategy(Figure 22).73 percent consider the strategy In contrast to the defensive data strategy,the offen-sive data strategy aims to maximize the value of data and optimize its use to achieve business advantages.With an offensive strategy,the focus is on gaining in-sights and fostering innovation.The goal is to derive strategic advantages from available data and enhance competitiveness.Participants were asked to what ex-tent the offensive data strategy is considered helpful in 46Industry 4.0 Barometer 2025 /Results of the Survey47disagreesomewhat disagree.have useful and valuable insights into our core processes.are more responsive in the market and production.can make decisions faster and more reliably.are more economically successful.somewhat agreestrongly agreeagreeneutralstrongly disagreestrongly disagreedisagreesomewhat disagreeneutralsomewhat agreeagreestrongly agreeFigure 22:Corporate offensive data strategyFigure 23:Data usage compared to competitorsMy companys offensive data strategy helps to.generate additional revenue with existing products and services.respond quickly to competitors and market changes.create new products and services.leverage new data sources(internal or external).monetize company data.Compared to our competitors,my company manages and utilizes data better allowing us tohelpful in developing new products and services,as well as in reacting quickly to competition and market changes.72 percent also rate it as supportive in utiliz-ing new data sources.An offensive data strategy not only enables companies to generate additional reve-nue with existing products and services but also fos-ters innovation and responsiveness to market changes.However,when it comes to the direct monetization of corporate data,only 50 percent of respondents agree.A possible reason for this could be the increas-ing awareness of the actual monetization potential of data.Particularly in the manufacturing sector,the idea persisted that data from machines could be sold di-rectly as additional assets.This assumption has proven to be unrealistic,as data only becomes valuable in the context of the entire production process.It shows that companies are not willing to pay twicefor equip-ment and for its data.A well-thought-out data strat-egy lays the foundation for the targeted use of data to optimize business processes and foster innovation.It is closely linked to the ability to realize data-driven competitive advantages.The next section will show how companies can strengthen their market position and achieve economic success through the strategic use of data.When comparing defensive and offensive data strat-egies,it is noticeable that most companies adopt a defensive approach.This is particularly evident in the DACH region,where the focus is primarily on risk and cost reduction,as well as compliance with regulatory requirements.However,such a one-dimensional focus leaves potential value creation and innovation oppor-tunities untapped.Chinese companies take a more balanced approach,combining the economic oppor-tunities of an offensive data strategy with appropriate risk and cost management.Data-Related Competitive AdvantageA data-related competitive advantage arises when companies strategically leverage data to make better decisions,increase efficiency,drive innovation,and respond more quickly to market changes.This can involve generating additional revenue from existing products and services,developing new products and offerings,unlocking new data sources,and monetiz-ing company data.Participants were asked to what extent their companies perceive a data-driven compet-itive advantage in relation to their competitors.The study examined whether the use of data leads to eco-nomic success,faster and more reliable decision-mak-ing,increased market and production responsiveness,and valuable insights into core processes.Overall,the majority of respondents perceive the im-proved use of data as a clear advantage for companies(Figure 23).It is particularly noteworthy that 80 per-cent of participants identify access to deeper insights into core processes as a key benefit.76 percent of re-spondents consider the ability to make faster and more reliable decisions as a key advantage of data-driven processes.In addition,74 percent regard increased re-sponsiveness in both the market and production as an important competitive edge.This demonstrates that data-driven technologies offer significant benefits at both the operational and strategic levels.Furthermore,76 percent of respondents agree that targeted use of data contributes to economic success.These findings highlight how data-driven approaches can enhance operational efficiency and agility.Data GovernanceData governance in companies refers to the strategic management and oversight of data administration to en-sure that data is used reliably,securely,and effi-ciently while also complying with legal requirements and internal policies.To meet these requirements,clearly defined roles and responsibilities are essential.The survey shows that,in terms of the highest level of agreement decision-making authoritiessuch as roles(e.g.,Chief Data Officer,Data Owner,Data Stew-ard,Data Custodian,Data Producer,Data Consumer)and committees(e.g.,Data Governance Council,Data Stewardship Team,Data Owner Committee)are clearly defined in only about 20 percent of companies(Figure 24).However,without clearly established role and committee concepts,there is a risk that data will not be managed and used consistently and system-atically across the company.Efficient,company-wide decision-making processes that align IT and business strategies are crucial for improving data-driven produc-tion processes.By contrast,companies demonstrate greater success in assigning specific decision-making authorities and tasks,even though these roles and re-sponsibilities are clearly defined in only about 20 per-cent of companies.Companies can assign decision-making authority and responsibilities regarding data either centralized or decentralized.A central data governance approach 48Industry 4.0 Barometer 2025 /Results of the Survey49more centralized than decentralizedmore decentralized than centralizeddecentralized approachcentralized approachFigure 24:Corporate data governanceFigure 25:Responsibilities and decision rights for the management and use of process dataMy company.How does your company assign responsibilities and decision-making authority regarding the management and utilization of process data from production?has established company-wide roles and committees for data governance.has clarified who is responsible for data-related tasks.has clearly assigned decision-making authority to regulate how data is managed and utilized.strongly disagreedisagreesomewhat disagreeneutralsomewhat agreeagreestrongly agreeis characterized by centralized control of data pol-icies and process-es,ensuring high data quality and consistency,but potentially limiting flexibility and de-cision-making speed.In contrast,a decentralized ap-proach places responsibility within individual business units,allowing for greater adaptability and faster de-cisions,but potentially leading to data inconsistencies.The results indicate that centralized approaches are more common in companies than decentralized ones(Figure 25).In an international comparison,the DACH region stands out with a particularly strong tenden-cy toward centralization.This suggests an attempt to transfer existing hierarchical structures from the physi-cal world into the digital realm.It raises the question of whether this tendency reflects a management culture focused on control and security.Here,the role of the CIO could help counterbalance this approach and fos-ter greater trust in data handling within the company,as the analysis on(see p.69 et seq.)demonstrates.In summary,the findings indicate that data governance in companies is not yet fully mature.Responsibilities in the form of roles and committees need further re-finement to ensure that corporate data is systemat-ically and comprehensively collected,managed,and utilized.Data ManagementData management in companies is crucial for system-atically collecting,storing,and analyzing information,enabling well-founded decisions and securing long-term competitive advantages.In this context,compa-nies were surveyed on aspects such as data quality,data availability,documentation of management pro-cesses,and metadata maintenance.Regarding data quality,76 percent of respondents stated that they can work adequately with the quality of their data(Figure 26).In an international comparison,the USA,the U.K.,and China show similar levels of data quality(7580 percent),whereas the DACH region significantly lags behind at 65 percent.The differences in data quali-ty assessments across these regions point to several influencing factors.Possible reasons include a lack of clear data ownership and insufficient data governance in some companies,which leads to lower data quality.Additionally,regulatory requirements,such as stricter data protection laws in certain regions,may restrict access to and processing of data,thereby affecting its quality.A relatively new and increasingly cited factor influencing data quality is the varying speed of arti-ficial intelligence(AI)implementation.AI has the ca-pability to clean,harmonize,and detect anomalies in data.Companies that invest less in AI may struggle to improve their data quality.Furthermore,a compa-nys system landscape and data storage can provide insights into the overall quality of its data.This is also reflected in the interviews conducted.For example,Thomas Speck,CIO of TRUMPF SE Co.KG,noted that decentralized data silos and an unclear tool-set negatively impact data quality and hinder the compre-hensive implementation of data-driven production.50Industry 4.0 Barometer 2025 /Results of the Survey51 Thomas Speck CIO(TRUMPF)TRUMPF SE Co.KGTRUMPF Brief Profile TRUMPF has a history spanning over 100 years.To-day,it is known for high-quality machine tools,lasers,electronics,and innovative system solutions that sup-port industrial companies worldwide.The company is headquartered in Ditzingen,northwest of Stuttgart,and employs around 19,000 people,approximately half of whom work in Germany.TRUMPF offers cus-tomized solutions and machines for high-performance production lines across various industries,including automotive,construction,healthcare,science,ener-gy,and aerospace.In the field of laser technology,TRUMPF is a global leader,operating in over 70 coun-tries.As a driver of innovation,TRUMPF is also ac-tively embracing digital transformation.The company not only optimizes internal processes but also devel-ops new data-driven and customer-centric business models to meet the demands of modern industry.Thomas Speck Brief BiographyThomas Speck is a business informatics specialist with more than 20 years of professional experience,and he has been the Chief Information Officer(CIO)of the German family-owned company TRUMPF SE Co.KG since June 2021.With a strong background in IT strategy,software development,and digitaliza-tion,he plays a key role in driving the companys dig-ital transformation.Participants:Thomas Speck(TRUMPF CIO),Julius Peters(MHP Manager Digital Factory and Supply Chain),Johanna Veit(MHP Senior Sales Director)Julius Peters:Can you give us a brief insight into your role and responsibilities at TRUMPF,particularly in light of your previous experience in IT and digita-lization?Thomas Speck:As CIO,I am responsible for global IT at TRUMPF,overseeing all IT-related topicsfrom business process management systems and classic in-frastructure to digitalization topics such as big data,software development,and AI development.A key focus of my work is transforming IT into a service-ori-ented function with a stronger emphasis on business value creation.I originally studied computer science and have a solid background as a software developer and IT program manager.Digitalization and IT trans-formation have been central themes throughout my career.Julius Peters:Generally speaking,how relevant do you think data will be in 2025?Thomas Speck:Data plays a crucial role for compa-nies,but more important than mere availability is the ability to leverage data effectively and establish the right foundations within the organization.Even 20 years ago,I was working with central data warehous-es and professional data management.Today,the key challenge is to collect and store data in a standard-ized and efficient way while ensuring high data qual-ity to create real added value and enable scalability.Julius Peters:Many companies face challenges in implementing AI effectively.What internal and exter-nal challenges do you see in this regard?Thomas Speck:Setting realistic expectations is cru-cial.In my experience,the main challenge lies in es-tablishing a consistent data architecture and gover-nance framework.For this purpose,we at TRUMPF have created a data integration platform as a central data marketplace that bundles data from business process management systems,unstructured sources,and IoT information.Ensuring data integrity and quality remains the big-gest challenge.Responsibilities for data,data gover-nance,and the traceability of data flows are also key factors.AI alone will struggle to resolve issues related to poor data quality or unstructured data manage-ment.Additionally,external regulatory requirements pose a significant challenge.Different data protection and compliance regulations in various markets necessi-tate a flexible yet robust data strategy to comply with legal frameworks.Julius Peters:You have emphasized the importance of a consistent data architecture and data quality.Given this,how would you assess the current state of the data infrastructure at TRUMPF,particularly in terms of efficiency and cost-effectiveness?Thomas Speck:Our platform has evolved over the years and is now highly efficient,serving as a crucial lever for implementing our digitalization initiatives.We have developed smart strategies to actively man-age cloud costs by optimizing cloud consumption and deciding which data to access and which to archive.For our end customers,we offer digital solutions in the form of software products and process optimiza-tions based on data,enabling more efficient produc-tion and reduced waste.A key factorboth internal-ly and for our customersis the networking of our systems.This allows us to automate production pro-cesses,increase machine availability,and ultimately enhance efficiency on the shop floor.Furthermore,we expect additional performance im-provements through the standardization of our busi-ness processes within our large-scale transformation programs,such as S4.Julius Peters:How advanced is process mining at TRUMPF in this context?Thomas Speck:We have explored the topic,but cur-rently,the S4 transformation takes top priority.Once we have completed the transition to S4,process mining could become relevantespecially to assess whether standard SAP processes are being followed or if new workarounds emerge.Overall,however,I think that you do not necessarily have to solve this with process mining.In my experience,a Business Process Management Organization(BPMO)with pro-cess owners,process managers,and consultants can also effectively manage this.Julius Peters:We briefly touched on the topic of cloud computing earlierdata security and server lo-cation are often debated.What is your take on this?Thomas Speck:In my experience,hyperscalers al-ready offer a high level of security and continuous-ly introduce new measures to improve data security.Additionally,we can also ensure a corresponding lev-el of security of our systems with our own resources.While internal misuse and data theft can never be completely ruled out,we have a tightly monitored se-curity system in place.Overall,we consider our data storage and processing security to be at a high level.Julius Peters:High security standards are essential,but at the same time,connectivity opens up new op-portunities.How does TRUMPF leverage these to op-timize machines and data usage?Thomas Speck:We aim to invest in our products,machine connectivity,IoT,and data engineering to efficiently connect our machines,extract valuable information,and assess it using AI.Classic use cas-es where we have already made significant progress include machine condition monitoring,predictive maintenance,and digital twins.Interview52Industry 4.0 Barometer 2025 /Results of the Survey53Johanna Veit:The topics you just mentioned are very machine-and production-oriented.How is your IT department involved in this area?Thomas Speck:IT provides central self-service plat-forms that allow users to develop applications in-dependently,while also offering support through consulting and technological expertise.In close col-laboration with business units,IT develops tailored software solutions and ensures their ongoing oper-ation.Julius Peters:To succeed as a data-driven company,deep collaboration across all areas and levels is cru-cial.The term“change management”often comes up in this context.What approaches do you take to ensure active employee engagement alongside their daily tasks?Thomas Speck:We anchor responsibility for data ob-jects directly within business units through our BPMO structurebusiness process owners,managers,and key users ensure compliance with data quality and governance standards.Additionally,we promote knowledge sharing on digital topics through working groups,workshops,and town halls,and provide tar-geted training on digital skills via HR.Julius Peters:Our research suggests that companies in the US and China are generally more experimental in terms of innovation and have a culture that em-braces failure.How do you view this aspect?Thomas Speck:Mistakes are part of the processthe key is to make them quickly,learn from them,and avoid repeating them.In our current AI initia-tive,we have many use cases,and it is clear that not all will deliver long-term economic value.Ideally,we quickly determine whether a business case is viable so that we can promptly pivot and try again.Julius Peters:You were named CIO of the Year 2023.What advice would you give to companies still in the early stages of transitioning to a data-driven production model?Thomas Speck:The path we have taken at TRUMPF over the past six years could serve as a good refer-ence.As a first step,we developed a data strategy and realized that we had too many decentralized data pools and BI tools.Therefore,we first consoli-dated the BI front-end landscape and set up a central big data environment in which all data objects are made available.Close alignment with business units was particularly important.Together,we agreed on a reference architecture to implement development projects within a structured framework and create synergies.This was a key success factor.It enables us as IT to act quickly and implement new digital solu-tions efficiently.If you are interested in the details,you can find out more in our white paper on the reference architecture.54Industry 4.0 Barometer 2025 /Results of the Survey55data follows widely adopted standards to ensure interoperability.production data is collected in the right quality and granularity to generate meaningful insights.we can easily integrate external data to improve production processes.we have real-time access to production data.we have a robust and scalable technological infrastructure for data collection and storage.strongly disagreestrongly disagreedisagreedisagreesomewhat disagreesomewhat disagreeneutralneutralsomewhat agreesomewhat agreeagreeagreestrongly agreestrongly agreeFigure 26:Data quality by regionFigure 27:Data technology as the foundation for data-driven productionIn my company,the quality of the data I work with is always appropriate(including accuracy,completeness,reliability,timeliness of data).In my company.Data Infrastructure and Technical CapabilitiesShifting the focus from the organizational level of data management to the technological level in the context of data-driven production,participants were first asked to assess their companys technological data infrastructure and capabilities.The results in-dicate that the technological data infrastructure is generally viewed positively,with 71 percent of re-spondents expressing agreement.This suggests that the majority of surveyed companies have the nec-essary technological foundations for implementing data-driven production.Real-time access to produc-tion data is particularly noteworthy,with the high-est agreement rate at 77 percent.This indicates that many companies are capable of capturing production data in real timean essential factor for efficient and flexible manufacturing.Standards ensuring interop-erability were also rated positively,with 74 percent agreement,suggesting that many companies have the necessary frameworks to seamlessly integrate various systems and technologies.Obtaining produc-tion data of an appropriate quality and granularity was also viewed positively by 74 percent of respon-56Industry 4.0 Barometer 2025 /Results of the Survey57disagreesomewhat disagreesomewhat agreestrongly agreeagreeneutralstrongly disagreemanagers take the use of data in decision-making for granted and avoid“gut feeling”decisions.employees have access to all relevant data to make informed decisions.sharing data across functions and departments is mandatory.new data-based insights lead to concrete changes.employees and employee representatives support the consistent use of data to achieve corporate goals.data is treated as a valuable asset.there is a culture of experimentation to explore new ideas and to challenge the status quo based on data.Figure 28:Key findings of data cultureIn my companydents,which shows that many companies are able to collect high-quality data.However,integrating external data and seamlessly consolidating it into a common repository2 received lower agreement rates of 67 percent and 68 percent,respectively.This may be due to differences in data formats that hinder smooth integration.These areas still offer room for improvement(Figure 27).Companies in the DACH region may still need to take steps to improve their technological data infrastructure and remain compet-itive.However,the survey overall indicates that many companies already have a solid technological founda-tion to support data-driven production.The positive feedback from respondents can likely be attributed to a combination of technological advancements,a stronger data-driven corporate culture,and improved data infra-structure.Trends such as the IoT,cloud technology,data quali-ty,and interoperability standards could enable com-panies to utilize data more effectively and create better-integrated systems.Additionally,ongoing dig-italization and strong management support may fur-ther drive improvements in data availability and uti-lizationas reflected in the surveys positive ratings.Data CultureA companys data culture defines its attitude and practices regarding data-driven decision-making and innovation.A strong data culture is characterized by the seamless integration of data-based decisions,the recognition of data as a valuable asset,and the cross-departmental sharing of information.Addition-ally,a culture of experimentation is encouraged to generate new insights and drive tangible change.Alongside the previously mentioned organizational foundations,a well-established data culture is a key prerequisite for data-driven production.With an agreement rate of 81 percent,the recognition of data as a valuable asset is particularly well received(Figure 28).This indicates a strong awareness of data as a strategic resource.Furthermore,the majority of employees and works councils have a positive attitude toward using data to achieve company objectives.However,a regional comparison reveals significant dif-ferences between the DACH region and other co
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Academic Editors:Ratna BabuChinnam and Sara MasoudReceived:3 March 2025Revised:17 March 2025Accepted:21 March 2025Published:24 March 2025Citation:Islam,M.T.;Sepanloo,K.;Woo,S.;Woo,S.H.;Son,Y.-J.A Reviewof the Industry 4.0 to 5.0 Transition:Exploring the Intersection,Challenges,and Opportunities of Technology andHumanMachine Collaboration.Machines 2025,13,267.https:/doi.org/10.3390/machines13040267Copyright:2025 by the authors.Licensee MDPI,Basel,Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution(CC BY)license(https:/creativecommons.org/licenses/by/4.0/).ReviewA Review of the Industry 4.0 to 5.0 Transition:Exploring theIntersection,Challenges,and Opportunities of Technologyand HumanMachine CollaborationMd Tariqul Islam,Kamelia Sepanloo,Seonho Woo,Seung Ho Wooand Young-Jun Son*Edwardson School of Industrial Engineering,Purdue University,West Lafayette,IN 47904,USA;islam70purdue.edu(M.T.I.);ksepanlopurdue.edu(K.S.);woo75purdue.edu(S.W.);woo44purdue.edu(S.H.W.)*Correspondence:yjsonpurdue.eduThese authors contributed equally to this work.Abstract:The Industrial Revolution(IR)involves a centuries-long process of economic andsocietal transformation driven by industrial and technological innovation.From agrarian,craft-based societies to modern systems powered by Artificial Intelligence(AI),each IR hasbrought significant societal advancements yet raised concerns about future implications.Aswe transition from the Fourth Industrial Revolution(IR4.0)to the emergent Fifth IndustrialRevolution(IR5.0),similar questions arise regarding human employment,technologicalcontrol,and adaptation.During all these shifts,a recurring theme emerges as we fear theunknown and bring a concern that machines may replace humans hard and soft skills.Therefore,comprehensive preparation,critical discussion,and future-thinking policiesare necessary to successfully navigate any industrial revolution.While IR4.0 emphasizedcyber-physical systems,IoT(Internet of Things),and AI-driven automation,IR5.0 aims tointegrate these technologies,keeping human,emotion,intelligence,and ethics at the center.This paper critically examines this transition by highlighting the technological foundations,socioeconomic implications,challenges,and opportunities involved.We explore the role ofAI,blockchain,edge computing,and immersive technologies in shaping IR5.0,along withworkforce reskilling strategies to bridge the potential skills gap.Learning from historicpatterns will enable us to navigate this era of change and mitigate any uncertainties inthe future.Keywords:artificial intelligence(AI);humanmachine collaboration;socioeconomicimplications;workforce reskilling;Industry 4.0;Industry 5.01.IntroductionThe IR is better understood as a process of economic transformation rather than a fixedperiod in a particular setting 1.This perspective acknowledges the spatial and temporalheterogeneity in adopting IR across global contexts.For instance,while regions such as theUnited States and Western Europe began undergoing their Second Industrial Revolution(IR2.0)by the late 19th century,other areas,particularly in Asia,including China,India,and Korea,did not commence their First Industrial Revolution(IR1.0)until the 20th century.However,Japan,despite being a latecomer to the IR1.0,accelerated its industrial growthduring the Meiji period,becoming a significant player in the IR2.0 by the early 20th centuryand laying the foundation for its later status as an Asian economic miracle 2.Such variations in the pace and timing of industrialization prove the importance ofviewing IR as a spectrum of changes rather than distinct events.Multiple industrial andMachines 2025,13,267https:/doi.org/10.3390/machines13040267Machines 2025,13,2672 of 34technological breakthroughs within this spectrum can overlap within specific geographicregions.Despite the disparate technological leaps across various IRs,several commonfactors serve as litmus tests for identifying a new IR.These factors include elevated levels ofproductivity,better transportation,demand for new soft and hard skills,resource augmenta-tion,political stability,and the availability of financial capital for investment.Furthermore,the interplay among these factors critically determines the pace and success of industrialtransformations in different regions.For example,the synergy between technologicalinnovation and the development of human capital can significantly accelerate economicgrowth,while inadequate infrastructure or political instability can hinder progress,leadingto uneven development.The precise start and end dates of IRs remain subjects of debate among historians asthe social and economic changes unfold at varying paces across different regions.However,historical analysis reveals four major shifts that have shaped our known civilization.IR1.0,or Industry 1.0,began in the late 18th century with the introduction of water and steam-powered mechanical manufacturing facilities.This era saw the transition from manualproduction methods to machines,which marked the beginning of industrialization.Theinvention of the steam engine by James Watt in 1769 was a pivotal moment,enabling themechanization of production processes and a new era of transportation 3.At first,thistransformation was seen as a cause of poverty and hardship because machines replacedhuman workers without proper protections or regulations.Companies and profit-drivenorganizations responded by reducing working hours and wages.However,this shift alsoled to major societal progress by improving workplace communication and increasingproduction rates,paving the way for future industrial advancements.The IR2.0,or Industry 2.0,emerged in the late 19th to early 20th century.This periodwas characterized by the widespread adoption of electricity and the development ofassembly lines in production 4.During this time,industries began to capitalize on naturaland synthetic resources(e.g.,rare earth elements,plastics,alloys,and chemicals),whichplayed a pivotal role in producing machinery and tools,paving the way for the automationof factory environments.Major advances during this period included(1)theintroduction ofsignal processing and its application intelephone communication in the 1870s,(2)structuralimprovements utilizing steel for buildings that resulted in the construction of the firstskyscrapers,as well as(3)innovations such as phonographs and motion pictures in the1890s.Additionally,the introduction of generators and refrigerators gradually replacedthe water and steam-powered engines of the IR1.0,which marks a significant transition inenergy utilization and production capabilities.The Third Industrial Revolution(IR3.0),or Industry 3.0,began in the 1970s and isoften referred to as the Digital Revolution.This era witnessed the rise of electronics,in-formation technology,and automated production.The development of programmablelogic controllers(PLCs)and robotics significantly enhanced automation within manufac-turing processes 4,5.Moreover,the introduction of computers and the internet laid thefoundation for the digital transformation of industries and set the stage for the subsequentphase of industrial evolution.One notable consequence of this IR3.0 was the contraction ofthe blue-collar job market,driven by widespread automation and increased productivity.However,this reduction was not uniform and was geographically localized.The primaryreason was that Western nations began to outsource production to relatively low-wagecountries,which led to the proliferation of labor-intensive manufacturing jobs within theAsian economy.The IR4.0,or Industry 4.0,started in the early 2000s and represents a leap in man-ufacturing and industrial practices characterized by the integration of advanced digitaltechnologies into production processes.Industry 4.0 marked the integration of cyber-Machines 2025,13,2673 of 34physical systems,IoT,big data analytics,cloud computing,and AI,leading to the emer-gence of smart factories that optimize efficiency,automation,and data-driven decision-making 68.IR4.0 technologiesoffered significant opportunities while simultaneouslyposing considerable challenges.On the one hand,organizations could leverage thesetechnologies to improve decision-making processes,enhance productivity,and reduce op-erational costs 9,10.For instance,the integration of sensor technologies enabled real-timemonitoring and control of manufacturing processes,which improved product quality andminimized waste 9.Despite the advantages of Industry 4.0,small and medium enterprises(SMEs)encountered major challenges,including limited financial resources,workforceskill gaps,and resistance to technological adoption.The high costs of implementation,coupled with a lack of expertise,created barriers to integrating advanced automation andAI-driven decision-making 1113.Nevertheless,the impact of Industry 4.0 extendedbeyond just operational improvements;it also had broader implications for sustainabilityand environmental responsibility.The adoption of Industry 4.0 practices contributed tosustainable manufacturing by optimizing resource use and minimizing waste 14,15.Building on the foundations of Industry 4.0,Industry 5.0 is emerging as a newparadigm rather than a mere extension.Unlike past industrial revolutions that unfoldedover centuries,the rapid pace of technological breakthroughs today justifies recognizingIR5.0 as a distinct era.From that point of view,IR5.0 builds upon the IR4.0 paradigm byemphasizing humanmachine cooperation as a central tenet.Industry 5.0 builds on theautomation and digitization of Industry 4.0 by prioritizing humanmachine collaboration.Rather than solely focusing on efficiency,IR5.0 emphasizes the integration of human cogni-tive abilities,adaptability,and ethical considerations into industrial systems,fostering amore balanced synergy between humans and technology 16,17.Some key components of IR5.0 are the human-centric approach,circular economy,andenhanced resilience.This paradigm focuses on the welfare of humans and augmentingus through technology 18.For example,collaborative robots(co-bots)are designed toundertake repetitive and hazardous tasks,enabling human workers to focus on more inno-vative and value-added responsibilities 19.This technological support increases workersoccupational satisfaction and motivates them to enhance their creative problem-solvingabilities 20.Another defining characteristic of Industry 5.0 is its commitment to sustain-ability and the circular economy.Recognizing the planets finite resources,IR5.0 prioritizesenergy-efficient production,waste reduction,and ethical industrial practices.Emergingtechnologies such as AI,IoT,and blockchain are increasingly adopted to enhance resourceoptimization,cybersecurity,and environmental responsibility in manufacturing 21,22.Similarly,resilience,which is the capacity of systems to maintain constant operations in theface of uncertainty or crisis events,plays a pivotal role in IR 5.0.In this context,resilience isnot just about recovery but also involves the proactive adaptation and evolution of systemsto withstand disruptions across the industry.For example,the widespread use of AI indata acquisition,interpretation,and evaluation in IR5.0 strengthens supply chain networksby implementing advanced technologies such as predictive disruptions,maintenance,an-ticipating potential failures,and minimizing downtime,thus making the whole supplychain more resilient.Researchers and industry practitioners are diligently working to ensure the successfulintegration of these key components in this new era of IR5.0.Continuous efforts to pushthe boundaries of our capabilities and knowledge are essential to achieve this.Therefore,this paper seeks to establish a critical assessment for understanding the natural progressionfrom the machine-driven,automated environments characteristic of IR4.0 to the morehuman-centric vision of IR5.0,where collaboration between humans and machines becomesparamount.In this review paper,we discuss the breakthroughs that are visibly leadingMachines 2025,13,2674 of 34us toward this goal as well as those that remain relatively unknown.This discussionincludes insights from multidisciplinary applications(i.e.,science,engineering,ergonomics,psychology,and ethics)and addresses the technologies that are going to shape the worldwe want to inhabit over the next few decades.In particular,we address the application ofIoT,big data,physics-informed machine learning,additive manufacturing,robotics,andhumanmachine interaction.Furthermore,we discuss advancements in AI,explainable AI,and cyber-physical systems,especially in terms of vulnerabilities and informed decision-making.Throughout each major IR,concerns regarding job security and the necessityfor upskilling have been prominent issues,which this review paper also addresses.Wewill discuss the tools likely to emerge at the forefront of this revolution and examine howupskilling the workforce in utilizing these tools(e.g.,extended reality(XR),braincomputerinterfaces,generative AI,humancomputer interaction,and blockchain)will benefit futuregrowth and adaptation.By analyzing past trends and emerging technological shifts,thisstudy provides critical insights into the challenges and opportunities defining the transitionto Industry 5.0.The remainder of this paper is structured as follows.Section 2 discusses the methodsused to determine the scope of this research.Section 3 explores the technological founda-tion of Industry 4.0,detailing key advancements such as IoT,big data,and cyber-physicalsystems.Section 4 discusses the emergence of Industry 5.0,highlighting the shift towardhumanmachine collaboration,sustainability,and resilience.Section 5 examines the so-cioeconomic implications of this transition,including workforce upskilling and ethicalconsiderations.Section 6 presents key tools and techniques that facilitate this shift,whileSection 7 outlines real-world applications and opportunities across various industries.Finally,Section 8 addresses the challenges and future directions of Industry 5.0,concludingwith insights on the evolving industrial landscape.2.MethodsTo identify the scope of this review paper,we conducted an extensive bibliometricnetwork analysis.Initially,we retrieved over 30,000 documents from the Scopus databasewith the keyword“Industrial Revolution”,then filtered them down to approximately19,000 documents,to include only articles,conference papers,and book chapters.The title,abstract,keywords,and author information of these selected documents were exportedin RefWorks(RIS)format.The collected bibliometric data were then sorted,analyzed,and visualized using VOSviewer software 1.6.20 as shown in Figure 1,which is a widelyused tool for constructing and visualizing bibliometric networks for journals,authors,and keywords.The bibliometric networks can illustrate different types of relationships,including citations,keywords co-occurrence,co-citations,and co-authorships.In thesenetwork visualizations,each item is represented by its label,and the size of each circlereflects the significance or frequency of the keyword or author.The larger the circle,thegreater the weight or frequency of the item.Each color represents a cluster of closelyrelated items,and the distance between two keywords approximately indicates theirrelatedness based on co-occurrence;the closer the keywords are to each other,the strongertheir connection.The keyword co-occurrence visualization shown here from the selected19,000 Scopus-indexeddocuments on“Industrial Revolution”served as a guideline tooutline the scope of this paper.From our analysis,we identified three primary clusterswithin the keyword map:one centered around Industry 4.0,another focused on AI and theIoT,and the third emphasizing sustainability and human-centered approaches.Machines 2025,13,2675 of 34Machines2025,13,xFORPEERREVIEW5 of 35educationandtraining,andaugmentedreality.Closelylinkedtothisistheclusterfocus-ingonAIandIoT,whichincludesmachinelearning,deeplearning,blockchain,cyberse-curity,andcyber-physicalsystems,whichhighlightstheroleofAI-drivenautomationinindustrialtransformations.AnemergingpresenceofIndustry5.0,positionedbetweenthesetwomajorclusters,suggestsagradualshiftfrompureautomationtowardsmorecollaborativeinteractionsbetweenhumansandAIsystems.Thethirdmajorclusterem-phasizessustainabilityandsocietalimpacts,placinghumansatthecore.Thisclusterin-cludescriticaltopicssuchassustainabledevelopment,climatechange,circulareconomy,andeconomicgrowth.Thisreflectstheincreasingemphasisonbalancingtechnologicalprogresswithenvironmentalandsocialresponsibility.Thevisualizationalsohighlightshistoricalandeconomicdimensionsofindustrialrevolutionsthroughkeywordshistory,economics,energy,agriculture,andthatindicatesthecurrentresearchextendsbeyondpurelytechnologicalaspects.Figure1.Bibliometrickeywordco-occurrencenetworkof“industrialrevolution”research.Similarly,wegeneratedabibliometricauthornetwork(Figure2)whichhighlightstheprominentresearchercollaborationsandthematicgroupingsintheindustrialrevolu-tionliterature.Here,severaldistinct,interconnectedclusterswereidentifiedandrepre-sentedbyaspecificcolor.Asshowninthelarge,denselyconnectedredcluster,whichidentifiesacoregroupofhighlyinfluentialauthorswhofrequentlycollaboratewithintheirrobustcollaborativenetwork.Smallerclustersofvariouscolors(suchasblue,green,andpurple)reflectadditionalresearchergroups,likelyindicatingregionalorthematicspecializations(e.g.,agriculture,energy,history,biasness,economics).Additionally,thepresenceofsmallerorisolatedclusterssuggestsemergingresearchtopicsorspecializedareasthatarecurrentlyperipheralbutmayrepresentpromisingdirectionsforfuturere-search.Figure 1.Bibliometric keyword co-occurrence network of“industrial revolution”research.The most dominant research cluster we observed is Industry 4.0,a central themeconnecting multiple domains such as smart manufacturing,supply chains,digitalization,education and training,and augmented reality.Closely linked to this is the cluster focusingon AI and IoT,which includes machine learning,deep learning,blockchain,cybersecu-rity,and cyber-physical systems,which highlights the role of AI-driven automation inindustrial transformations.An emerging presence of Industry 5.0,positioned betweenthesetwo majorclusters,suggests a gradual shift from pure automation towards morecollaborative interactions between humans and AI systems.The third major cluster em-phasizes sustainability and societal impacts,placing humans at the core.This clusterincludes critical topics such as sustainable development,climate change,circular economy,and economic growth.This reflects the increasing emphasis on balancing technologicalprogress with environmental and social responsibility.The visualization also highlightshistorical and economic dimensions of industrial revolutions through keywords history,economics,energy,agriculture,and that indicates the current research extends beyondpurely technological aspects.Similarly,we generated a bibliometric author network(Figure 2)which highlights theprominent researcher collaborations and thematic groupings in the industrial revolutionliterature.Here,several distinct,interconnected clusters were identified and representedby a specific color.As shown in the large,densely connected red cluster,which identifiesa core group of highly influential authors who frequently collaborate within their robustcollaborative network.Smaller clusters of various colors(such as blue,green,and purple)reflect additional researcher groups,likely indicating regional or thematic specializations(e.g.,agriculture,energy,history,biasness,economics).Additionally,the presence of smalleror isolated clusters suggests emerging research topics or specialized areas that are currentlyperipheral but may represent promising directions for future research.Machines 2025,13,2676 of 34Machines 2025,13,x FOR PEER REVIEW 6 of 35 Figure 2.Bibliometric author collaboration network in“industrial revolution”research.3.Technological Foundation of IR4.0 The technological foundation of IR4.0 is primarily based on the convergence of IoT,digital twin of industrial processes,cloud computing,robotic systems,and advanced an-alytics.However,all these technologies did not appear overnight.Rather,they gradually matured for decades and reached a point where seamless integration became feasible at scale.For instance,sensor technologies have existed for years,but their miniaturization and plummeting costs now enable real-time,accurate data collection across industries.Similarly,the once-theoretical concepts of digital twins and XR(Extended Reality)have become increasingly applied and fundamentally altered how products are designed,tested,produced,and consumed.This shift from isolated technological breakthroughs to interconnected,data-rich ecosystems laid the foundation for the current time in which every machine,process,and worker is digitally aware of and capable of their improve-ment.3.1.IoT IoT is one of the key components of IR4.0,which represents the interconnectedness of the device network to exchange and transmit data 23.In an industrial environment,IoT allows interaction between devices,sensors,equipment,and systems,also called the Industrial Internet of Things(IIoT).IoT provides real-time insights,stimulates automation of decision-making,and helps to innovate manufacturing or supply chain processes 24.One of the most popular IoT contributions to IR4.0 is real-time monitoring of the process.Sensors equipped in machines collect multiple streams of data,such as temperature,vi-bration,pressure,and humidity,to ensure the system operates within designated thresh-olds.Nowadays,companies worldwide utilize IoT smart devices to monitor equipment performance,predict the necessity of maintenance,and perform diverse functionalities to reduce operation idle time and increase productivity 23.Figure 2.Bibliometric author collaboration network in“industrial revolution”research.3.Technological Foundation of IR4.0The technological foundation of IR4.0 is primarily based on the convergence of IoT,digital twin of industrial processes,cloud computing,robotic systems,and advanced ana-lytics.However,all these technologies did not appear overnight.Rather,they graduallymatured for decades and reached a point where seamless integration became feasible atscale.For instance,sensor technologies have existed for years,but their miniaturizationand plummeting costs now enable real-time,accurate data collection across industries.Similarly,the once-theoretical concepts of digital twins and XR(Extended Reality)havebecome increasingly applied and fundamentally altered how products are designed,tested,produced,and consumed.This shift from isolated technological breakthroughs to inter-connected,data-rich ecosystems laid the foundation for the current time in which everymachine,process,and worker is digitally aware of and capable of their improvement.3.1.IoTIoT is one of the key components of IR4.0,which represents the interconnectednessof the device network to exchange and transmit data 23.In an industrial environment,IoT allows interaction between devices,sensors,equipment,and systems,also called theIndustrial Internet of Things(IIoT).IoT provides real-time insights,stimulates automationof decision-making,and helps to innovate manufacturing or supply chain processes 24.One of the most popular IoT contributions to IR4.0 is real-time monitoring of the process.Sensors equipped in machines collect multiple streams of data,such as temperature,vibra-tion,pressure,and humidity,to ensure the system operates within designated thresholds.Nowadays,companies worldwide utilize IoT smart devices to monitor equipment perfor-mance,predict the necessity of maintenance,and perform diverse functionalities to reduceoperation idle time and increase productivity 23.In addition,IoT contributes to process optimization by supporting remote access anddistinguishing process bottlenecks,root causes,and potential improvement areas.Withinsupply chain management framework,IoT provides visibility among supply chain entitiesMachines 2025,13,2677 of 34by generating smart logistic solutions from material to product delivery.Production floorIoT devices such as Radio Frequency Identification(RFID)25,Ultrawide Band(UWB)26,Global Positioning System(GPS),vision systems,condition monitoring sensors,proximitysensors,pressure sensors,temperature sensors,actuators help to track the work in progressstatus,locate objects,identify bottlenecks,manage inventory and maintain safety andsecurity.However,despite having advancements and widespread use cases in IoT,thereare major challenges such as data security,interoperability,and expansion 27.Research iscurrently being conducted to ensure better encryption,standardized protocols,and edgecomputing to overcome those challenges.In addition to IoT technologies that enable device interconnectivity and real-time man-agement of industrial systems,humanmachine interaction(HMI)is increasingly essentialin industrial environments.HMI acts as a critical component within the IIoT framework,particularly enhancing real-time process monitoring and bridging the gap between humanoperators and automated systems.Industrial environments frequently utilize HMIs forintuitive and efficient operator control,often integrating them seamlessly with SupervisoryControl and Data Acquisition(SCADA)systems.SCADA systems facilitate centralized datacollection,process visualization,and remote control over multiple operations.Additionally,Graphical User Interfaces(GUIs)improve usability by providing interactive dashboardsthat display real-time data,alerts,and processing insights which enables quicker andbetter-informed decision-making by users.3.2.Big DataNowadays,it is quite common that thousands of tiny sensors on a production floorgenerate such vast amounts of data that traditional storage and analysis methods cannotcope.Modern manufacturing encounters both its challenges and greatest opportunitiesin this domain.Clive Humby stated,“Data is the new oil”,although some argue data areeven more valuable 28.By transforming raw streams of logs,readings,and performancemetrics into actionable insights,the utilization of big data has become a true game changerfor IR4.0.The amount of data generated worldwide has exploded,and its promise to driveproductivity growth is visible in every sector 29.The integration of big data analytics approaches and frameworks allows predictiveanalysis,through which organizations can predict anomalies proactively.Some populartools at present are Apache Hadoop ecosystem,Apache Spark,Time series databases,Azure IoT analytics,NoSQL databases,and communication protocols like Message Queu-ing Telemetry Transport(MQTT),which facilitates lightweight messaging and streaming.Smart machines linked to centralized systems can dynamically transmit data that can beanalyzed and integrated to forecast potential failures 30.The deployed algorithms analyzeboth historical and current data streams to identify potential patterns of anomalies.Thisapproach is extensively used in credit card fraud detection,demand forecasting,inventorymanagement,intrusion detection,cybersecurity,and manufacturing.Consequently,theseinterconnected IoT devices consistently generate diverse data and support quality man-agement processes through real-time online anomaly detection,thus ensuring seamlessproduction with enhanced quality products 31.3.3.Digital Twin of Industrial ProcessesThe concept of digital twins was once considered science fiction,but it is now a real-ity with many real-world applications.Digital twins essentially generate virtual replicasreflecting physical entities,processes,and systems.This digital replica is interconnectedwith physical systems in real-time through sensors and datasets to perform simulation,analysis,and optimization tasks of the process by utilizing Cyber-Physical Systems(CPS)Machines 2025,13,2678 of 34concept 32.Rather than relying on theoretical models or best-guess estimates,bothvirtual and physical entities can pull in large amounts of real-time actual sensor or simu-lated data and allow engineers to experiment with new configurations to predict potentialbreakdowns in a no-risk virtual sandbox.This dual reality not only reduces the time andcost typically associated with iterative prototyping but also creates a feedback loop thatcontinuously refines itself and increases efficiency and resilience 33.For instance,inthe automotive manufacturing process,such twins assess new materials,compositions,production methods,and innovation.In the production aspect,such twins can optimizeprocesses by monitoring real-time data,detecting insufficiency,and predicting potentialfailure by comparing them with the standard virtual twin.NVIDIA,one of the leaders inthis field of digital twin architecture,recently introduced“Mega”,a blueprint within theOmniverse designed to help develop,test,and optimize AI and robotic fleets on a largescale in a digital twin environment before any real-world implementation 34.Advancedwarehouses and factories are now using extensive virtual fleets of autonomous mobilerobots(AMRs),robotic arm manipulators,and humanoid robots working alongside humanoperators 35 for testing and validation before implementation(Figure 3).This demandsthorough simulation-based training to streamline operations,enhance safety,and reduceinterruptions.Such a continuous data acquisition environment provides a manufacturingprocess with automated derivation of optimization measures and parameters 36.More-over,remote control has emerged as a significant application in manufacturing,defense,and healthcare.It can lower non-value-added transportation costs and ensure acceptablesafety in systems where local access is limited and hazardous 37.Machines 2025,13,x FOR PEER REVIEW 8 of 35 with physical systems in real-time through sensors and datasets to perform simulation,analysis,and optimization tasks of the process by utilizing Cyber-Physical Systems(CPS)concept 32.Rather than relying on theoretical models or best-guess estimates,both vir-tual and physical entities can pull in large amounts of real-time actual sensor or simulated data and allow engineers to experiment with new configurations to predict potential breakdowns in a no-risk virtual sandbox.This dual reality not only reduces the time and cost typically associated with iterative prototyping but also creates a feedback loop that continuously refines itself and increases efficiency and resilience 33.For instance,in the automotive manufacturing process,such twins assess new materials,compositions,pro-duction methods,and innovation.In the production aspect,such twins can optimize pro-cesses by monitoring real-time data,detecting insufficiency,and predicting potential fail-ure by comparing them with the standard virtual twin.NVIDIA,one of the leaders in this field of digital twin architecture,recently introduced“Mega”,a blueprint within the Om-niverse designed to help develop,test,and optimize AI and robotic fleets on a large scale in a digital twin environment before any real-world implementation 34.Advanced ware-houses and factories are now using extensive virtual fleets of autonomous mobile robots(AMRs),robotic arm manipulators,and humanoid robots working alongside human op-erators 35 for testing and validation before implementation(Figure 3).This demands thorough simulation-based training to streamline operations,enhance safety,and reduce interruptions.Such a continuous data acquisition environment provides a manufacturing process with automated derivation of optimization measures and parameters 36.More-over,remote control has emerged as a significant application in manufacturing,defense,and healthcare.It can lower non-value-added transportation costs and ensure acceptable safety in systems where local access is limited and hazardous 37.Figure 3.Coordinated integration of human and robotic systems within a digital twin facility in NVIDIA Omniverse in collaboration with Accenture and KION Group 32.3.4.Physics-Informed Machine Learning(PIML)PIML integrates machine learning models with principles of physics,designing algo-rithms that adhere to the governing laws of thermodynamics,fluid dynamics,and mate-rials science 38.Training deep neural networks requires large datasets,which are not always accessible.Here,the laws of physics serve as a complement to training the neural network of low-dimensional data.PIML facilitates the simulation or analysis of systems by leveraging limited datasets from complex physical systems.One primary application Figure 3.Coordinated integration of human and robotic systems within a digital twin facility inNVIDIA Omniverse in collaboration with Accenture and KION Group 32.3.4.Physics-Informed Machine Learning(PIML)PIML integrates machine learning models with principles of physics,designing algo-rithms that adhere to the governing laws of thermodynamics,fluid dynamics,and materialsscience 38.Training deep neural networks requires large datasets,which are not alwaysaccessible.Here,the laws of physics serve as a complement to training the neural net-work of low-dimensional data.PIML facilitates the simulation or analysis of systems byleveraging limited datasets from complex physical systems.One primary application areaof PIML is simulation acceleration,which is highly expensive in computation.Althoughthere has been good progress in simulating multi-physics problems using the numericalMachines 2025,13,2679 of 34discretization of partial differential equations(PDEs),the incorporation of noisy data intoalgorithms poses numerous challenges.These difficulties arise from the complexity of meshgeneration and the high-dimensional constraints influenced by parameterized PDEs.PIMLresearchers are actively working to overcome these obstacles by merging data with mathe-matical models and implementing them through neural networks or other kernel-basedregression methods 39.Currently,there is a lot of research focusing on the integrationof PIML in predictive maintenance,manufacturing process control 40,41,metal additivemanufacturing 42,43,climate modeling 44,prognostics,and health management 45.3.5.Additive ManufacturingAdditive manufacturing(AM),commonly referred to as rapid prototyping,3D print-ing,layer manufacturing,and solid freeform fabrication,has transformed traditional man-ufacturing paradigms over the past decade 46.AM is used in a variety of industries torapidly develop a representation of design factors through a 3D model or prototype beforereleasing the products.The foundation of AM includes a three-dimensional computer-aided design to fabricate an object 47.This method of manufacturing demonstratedsignificant cost-effectiveness and architectural flexibility due to the capability to createcomplex geometric structures for manufacturing customized products that were previouslyimpossible with traditional manufacturing technologies.Additionally,various types ofmaterial sources,including liquid,filament,powder,and solid sheet,are utilized to enhancemanufacturing through AM.These materials opened a new era for applications in energy,automotive,aerospace,and biomedical fields 46.For example,AM is utilized to createpersonalized patient-specific implants,substituting hard tissues or bones by fabricatingbiocompatible mesh arrays 48.Despite its initial focus on applications in producingrelatively small-scale and intricate parts advancements in technology,decreasing materialcosts,and the broadening of applications have led to the increasing utilization of AM acrossvarious sectors,ranging from miniature robots 49 and prosthetics 50 to large structuressuch as houses and boats 51.3.6.Robotic SystemsRobotics systems are another important node in the digitally connected network ofIR4.0.Todays robotic systems are not the bulky,caged industrial robots we used to seedecades ago,rather,they are more mobile,agile,collaborative,and work alongside humansas assistants.The adoption of robotics in IR4.0 has significantly increased over the lastdecade due to more efficient algorithms,machine vision technology,upgraded sensors,andthe development of lighter,less expensive,and more powerful chips.These advancementsaimed to enhance productivity and produce high-quality products with great accuracy ina short amount of time 52.One of the key contributions of robotics systems for IR4.0 isprocess automation.Automated Guided Vehicles(AGVs)are a popular type of robotics thatcan move and operate autonomously.They are used in industrial applications to transportheavy or hazardous materials in factories and warehouses.Common types of AGVs inthe manufacturing process include fork trucks,unit loaders(AGVs with roller tables fortransporting trays),and tuggers(AGVs that pull carts)53.This type of small roboticsystem can reduce the physical effort involved in handling heavy loads and compensatefor the limited strength of human operators 54.However,while existing AGVs follow predefined paths,the emergence of AMRsshows a more efficient and intelligent approach to material handling.AMRs utilize ad-vanced AI,computer vision-based navigation,and adaptive decision-making algorithms towork dynamically compared to existing AGVs.The AMRs show improved autonomy inour industrial environments.Moreover,robotic systems offer consistency via automatedMachines 2025,13,26710 of 34solutions that minimize performance errors.Due to automation and remote-control capa-bilities,we can operate robotic systems in different environments to ensure consistency androbustness 55.Nowadays,collaborative robots(co-bots)of various forms are integratedinto the industrial framework,offering safety and efficiency while working alongsidehuman operators.3.7.Immersive TechnologiesImmersive technology or XR is a broad terminology that includes AR(AugmentedReality),VR(Virtual Reality),and MR(Mixed Reality).These technologies merge physi-cal environments with virtual worlds and enable users to have intuitive and immersiveexperiences.Industries have heavily invested in XR to improve training,manufacturingprocesses,and operational efficiency.As XR systems in the industry evolve,they show thepotential for a future where virtual environments integrate with daily routines.Allowingindividuals to attend professional meetings,remote factory visits,and even doctor visitswithout physical presence.Ongoing research and developments in this field show thatthese technologies are increasingly applied to enhance user experiences in education 56,marketing 57,entertainment 58,and healthcare 59.For example,AR has been usedin medical task simulations,demonstrating its potential in healthcare training and designevaluation 60.Moreover,machine learning models can be leveraged to enhance XR-basedtraining by optimizing real-time decision support and predictive analytics for medicalsimulations 61.AsXRapplicationscontinuetoexpand,researchonuserexperienceinhumanmachineinteraction has gained significant attention.Effective interaction design in XR environmentsrequires an understanding of cognitive load,usability,and engagement to enhance usersatisfactionand taskperformance.RecentUXresearchtrends emphasizeadaptiveinterfacesthat respond to users physiological and behavioral cues to ensure a more seamless andpersonalized experience 62.Advancements in haptic feedback,eye-tracking,and spatialaudio are being integrated to improve immersion and reduce sensory conflicts that canlead to discomfort or fatigue.Therefore,focusing on user experience in XR settings isbecoming crucial for optimizing interaction flow,reducing cognitive strain,and ensuringthat virtual environments align with users expectations and real-world applications.TheseUX-driven insights play a key role in refining XR technologies to create intuitive,efficient,and engaging experiences across various industries.4.Emergence of IR 5.0While we benefit from the connectivity and automation introduced by Industry 4.0,a new paradigm is emerging that transcends technology.IR5.0,the next phase of theindustrial revolution,places human creativity and well-being at its core,driven by dataanalytics and AI,while emphasizing sustainability,resilience,and ethical responsibility.Building on the solid technological foundation of IR4.0,IR5.0 recognizes that industrialrevolutions are neither abrupt transformations nor rigid binaries,but rather ongoingprogressions shaped by our needs and available resources.As we strive to meet thoseneeds,we risk exhausting our mental and physical capacities with information overload,and we aim to tackle this challenge in this new era by human-centric innovation.Thisrevolution also addresses environmental concerns by emphasizing sustainability and thecircular economy,which were absent in previous eras.All these advancements are helpingus address complex engineering challenges and medical inquiries and enabling us to livelonger and even envision life beyond our planet.Machines 2025,13,26711 of 344.1.Symbiosis of Human and Machine IntelligenceThe symbiosis between humans and machines involves collaboration to achieve re-sults that surpass individual limits.We humans are sentient beings,while machines arepreprogrammed to perform specific tasks repeatedly.Essentially,humans can be viewedas machines with consciousness,and both possess different types of intelligence.Just assomeone who is color blind cannot understand the concept of a rainbow,no matter howdetailed the description is,machines powered by the most advanced AI similarly cannotinherit consciousness.Machines can produce reasonable outputs across various formats,but this is merely the statistical mapping of their learning.Due to these limitations,thissymbiosis is important,where machine intelligence excels in high-speed processing,pat-tern recognition,and predictive analytics,allowing humans to focus on tasks that requireinsight,creativity,and ethical judgment 63.For example,in advanced manufacturingenvironments,it is common that AI-driven systems monitor real-time production datato uncover hidden inefficiencies,while human engineers remain crucial for interpretingcontext-specific nuances and making strategic decisions 64.Likewise,clinical supportplatforms utilize machine learning algorithms to analyze extensive medical databases,helping physicians diagnose complex conditions more accurately and swiftly.However,the ultimate authority lies with medical professionals,who integrate empathy,ethicalconsiderations,and personal experience into patient care 65.We should nurture the ideathat diverse teams outperform homogeneous ones,similar to the collaboration betweenpeople and machines.We believe organizations can pursue two goals:first,creating anintellectual division of labor to enhance processing capabilities,and second,promotinga culture that embraces collaborative and trustworthy hybrid intelligence.By mergingthe rapid processing abilities of machines with human adaptability and moral reasoning,this collaborative approach can increase productivity and ensure that innovation remainshuman-centric and responsible.4.2.Emotional IntelligenceEmotional intelligence refers to ones ability to manage their own emotions whileunderstanding the emotions of those around them through self-awareness,self-regulation,motivation,empathy,and social skills.Leaders with high emotional intelligence can defuseconflicts,empathize with team members concerns,and foster an inclusive environmentwhere innovative ideas thrive 66.Across industries,emotionally attuned leaders excel atbalancing data-driven strategies with interpersonal nuances so that human considerationsare not overshadowed by technical objectives.This focus on human-centric skills alignswith the broader principles of IR5.0,where technological advancements and emotionalwell-being converge to create a more balanced,sustainable ecosystem.It was long believed that only humans possess emotional intelligence,setting us apartfrom machines.However,modern machines can leverage the vast amount of data avail-able to respond not just to raw data but also to multimodal outputs.One such use caseis emotional intelligence for computers,where machines can actively interpret emotionsthrough machine learning algorithms.Today,computers are becoming more adept at under-standing emotions through specialized research on emotional intelligence called affectivecomputing 67.Through affective computing,systems and devices can recognize,interpret,process,and simulate human experiences,feelings,or emotions.When computers arecapable of analyzing data such as facial expressions,gestures,tone of voice,and keystrokedynamics,researchers call this artificial emotional intelligence.This capability enableshumans and machines to interact more naturally,resembling human-to-human interac-tions.As the field of artificial emotional intelligence continues to evolve,many companiesactively use affective computing to enhance their services and products.Affectiva,anMachines 2025,13,26712 of 34emotion-recognition software company,enables advertisers and video marketers to gatherreal-time facial expressions through Affdex 68.By comparing these expressions with arobust emotion database and benchmarks,clients gain actionable insights to refine theircontent and media investments.Realeyes,meanwhile,uses webcams,computer vision,and AI to analyze viewers facial expressions when they watch videos,allowing brands likeCoca-Cola and Hersheys to evaluate and improve their advertisement performance 69.At the MIT Media Lab,BioEssence developed a wearable device that tracks changes inheart rate to identify stress,pain,or frustration and then emits calming scents to calmusers 70.Such advancement in artificial emotional intelligence is becoming increasinglyimportant to steer us toward a deeper understanding of human emotion so that emergingtechnologies remain closely aligned with human well-being.4.3.Environment,Sustainability,and Circular EconomyThe idea of smart factories,robots working alongside humans,and personalizedmass production may seem futuristic,but these innovations,either partially or fully,havebeen in existence for years.What sets IR5.0 apart is not just the integration of thesetechnologies but its focus on sustainable technology so that industrial progress alignswith our environmental responsibility.While IR4.0 focused more on digitization andautomation,IR5.0 represents a broader shift toward balancing technological advancementswith sustainable development.As was already discussed,IR5.0 does not view progresspurely through the lens of efficiency and speed.Instead,it acknowledges the urgent needto rethink how industries operate in a world with finite resources.This shift influenceseverything from energy policies and supply chain management to manufacturing processesand product life cycles.Governments and corporations now recognize that sustainability isno longer optional but essential for long-term economic and environmental stability.A strong commitment to sustainability is visible across industries.We can see thatcountries are increasingly investing in renewable energy sources such as solar,wind,hydropower,and nuclear alternatives to reduce dependence on fossil fuels.The autoindustry provides a clear example of this transformation.The global shift from internalcombustion engines to electric vehicles(EVs)is not just about reducing emissions butalso about redefining the energy sector.Just a decade ago,large-scale battery storagewas considered impractical,but advancements in lithium-ion technology have provenotherwise.According to the International Energy Agency(IEA)report,global energystorage demand is projected to rise from 850 GWh as of 2023 to 10 TWh by 2035 71.Ofthis demand,90%comes from automakers such as Tesla,BYD,General Motors,and Ford,as they are investing heavily in their fleet electrification 72,73.On the other hand,minersaround the world are working on extracting raw materials such as lithium,nickel,andcobalt to meet their rising demand 74.At the same time,traditional automakers such asToyota and Honda are also exploring hydrogen-powered alternatives to reduce long-termreliance on traditional fossil-based energy sources 75.In addition to energy and transportation,this focus on sustainability is reshapingthe entire manufacturing landscape as well.Consumers are now increasingly aware ofproduct life cycles,which is pushing industries to shift from the traditional“takemakedispose”model to a circular economy approach that emphasizes reuse,recycling,andremanufacturing 76.This shift is not limited to physical goods only,it extends to digitaltools,services,and even software.Companies are adopting reverse logistics systems tooptimize the collection and reusing of industrial and consumer waste 77.At the same time,advanced manufacturing methods,such as AM,are helping minimize waste by enablingprecise,on-demand production without the excess material loss associated with traditionalmanufacturing techniques.Machines 2025,13,26713 of 34Similarly,to support this sustainability philosophy,new business models are emergingacross industries.For example,instead of selling products outright,some companies areadoptinga“product-as-a-service”model78.AChineseelectricvehiclemanufacturer,NIO,has introduced an innovative battery-swapping model as an alternative to conventionalcharging 79.This approach addresses a key concern for EV owners,which is the lengthyrecharging process.It allows customers to quickly swap depleted batteries for fully chargedones in refueling stations.Additionally,it alleviates worries about battery longevity andperformance,offering a more seamless user experience and customer-centric businessmodel.Beyond convenience,this strategy also promotes sustainability by centralizingbattery ownership,which enables more efficient recycling and material reuse.Government policies are also playing a crucial role in shaping this transition.Regula-tory frameworks such as the European Green Deal,which aims for carbon neutrality by2050 80,and the U.S.Inflation Reduction Act(2022)81,which offers tax incentives forgreen technology,are accelerating the shift toward sustainable industry practices.In addi-tion,the United Nations Sustainable Development Goals(SDGs)82,particularly SDG 9(Industry,Innovation,and Infrastructure),SDG 12(Responsible Consumption and Produc-tion),and SDG 13(Climate Action)closely align with the sustainability and human-centricinnovation vision of IR5.0.Collectively,these international policy frameworks reinforcethe direction towards sustainable manufacturing practices,responsible consumption,in-novative infrastructure,and climate-conscious industrial growth,thereby supporting thebroader societal objectives that define Industry 5.0.4.4.The AI RevolutionToday,there is a growing emphasis on personalized solutions and human-centricinnovations.Every individual is unique,and we react differently to different stimuli,which makes generalized approaches increasingly outdated.Therefore,the demand forpersonalized solutions is growing,but such solutions require vast amounts of data.Datahave always existed,but not in abundance or in a usable format to make it useful forintelligent decision-making.Due to that,previously,we could not use data to extractmeaningful information with traditional statistical analysis or available AI tools to providehighly personalized solutions.This is where AI comes into play,with the immense powerof knowing the unknown and revealing the unseen.The concept of AI has existed forcenturies,frequently portrayed in science fiction as humanoid robots or supercomputersthat control the world.However,understandings of AI today are not just limited to robots,but an ecosystem powered by sensors,algorithms,and computational devices.The mathematical foundation of AI was laid by Alan Turing,who introduced theconcept of a Universal Machine,now known as the Turing Machine 83.Between the1950s and 1970s,early AI programs such as Logic Theorist 84 and General ProblemSolver attempted to solve mathematical and logical challenges.However,computationallimitations led to an“AI winter”where progress stalled.AI research revived in the 1980swiththeintroductionofmachinelearningandneuralnetworks,withsomeexcitingworksinbackpropagation 85,speech and image recognition,and robotic applications.Despite thisprogress,limited data and processing power continued to slow development.Followingthat,in the 21st century,a big transformation happened in this field,fueled by big data,advanced algorithms,and enhanced hardware capabilities.A breakthrough came in 2006,when Geoffrey Hinton pioneered groundbreaking deep learning research 86.In 2011,IBM Watson defeated Jeopardy!champions Ken Jennings and Brad Rutter 87,then in2012,the ImageNet 88 competition showed AIs ability to outperform humans in imagerecognition.In 2016,AlphaGo 89 shocked the world by defeating a Go grandmasterwith its very unusual but intelligent“Move 37”.Meanwhile,commercial AI assistantsMachines 2025,13,26714 of 34such as Siri and Alexa further embedded AI into everyday life and solidified AIs role inmainstream technology.The AI boom accelerated significantly in 2020 with the launch of OpenAIs GPT-3,which showcased AIs ability to process natural language at an unprecedented scale.This latest AI revolution is driven by two major forces:algorithmic advancements andhardware improvements.The transformer architecture,introduced by Google researchersin 2017 90,led to breakthrough AI applications such as ChatGPT,DALL-E,Metas LLaMA,Googles Gemini and many more.These technologies are now capable of generating art,predicting protein structures,and even performing basic human tasks using operator agentsthat seemed impossible just a few years ago 91.Meanwhile,hardware advancementshave helped massive AI computations,with companies such as NVIDIA,AMD,and Inteldeveloping chips capable of trillions of operations per second.Looking ahead,industriesare advancing toward agentic AI,which will essentially be an autonomous system capableof replacing human labor in tasks such as scheduling,coding,and web browsing.Theultimate goal is artificial general intelligence(AGI)92,which would allow AI to reasonand think across multiple domains like a human.Therefore,this shift in AI development isno longer just about making machines act like humans;it is about integrating AI into ourdaily lives,businesses,and industries.This recent advancement in AI research is accelerating the transition from IR4.0 toIR5.0 at an unprecedented pace.In previous industrial revolutions,technological shiftstook decades or even centuries to fully materialize.However,AI-driven automation,intelligence,and adaptability are compressing this transition into just a few years.Today,we see collaboration between humans and AI-powered co-bots in manufacturing,whererobots no longer just replace human labor but work alongside humans.Additionally,governments and corporations utilize AI to analyze climate data,predict natural disasters,and accelerate drug discovery,which typically takes years but can now be optimized byAI to find solutions within days or even hours.AI is also reshaping transportation andlogistics.We are witnessing the rise of driverless taxis,automated freight transport,andTeslas full self-driving technology,which push the boundaries of autonomous mobility.Meanwhile,in the energy sector,AI is optimizing grid management,forecasting renewableenergy,and improving battery efficiency,thereby contributing to sustainable industrialgrowth.All these products,tools,and advancements are not only related to efficiency andautomation;they are also about freeing up human time for more meaningful work,such asdecision-making,creativity,and innovation.As AI continues to evolve,the line betweenhuman and machine intelligence is blurring,which makes it essential for us to adapt,learn,and integrate AI into our skill sets.The current growth and pace clearly indicate thatupskilling is no longer optional but necessary to stay relevant in the workforce.Therefore,in this IR5.0 era,we need to work with AI to redefine how we work,innovate,and interactwith the world around us.5.Socioeconomic Implications of IR4.0 to IR5.0 TransitionThe transition from IR4.0 to IR5.0 represents a significant shift with a renewed focuson human-centricity,sustainability,and resilience 93.This transition is driving substan-tial changes in workforce requirements and skill sets across industries.In addition,theworkplace culture and employment approach are being reassessed.People are becomingincreasingly concerned about their physical and cognitive well-being.We are also observ-ing a faster innovation cycle,lower production costs,and new business models that areboosting our productivity and economic growth.Machines 2025,13,26715 of 345.1.Workforce UpskillingAs automation and digitalization advances,there is an increasing need for workers toadapt and acquire new competencies.According to a study by the World Economic Forum,by 2030,59%of all employees will need reskilling due to the adoption of changing tech-nologies 94.It is now essential for workers to go beyond technical skills and conventionalqualifications;they must also cultivate adaptability,creativity,and technological fluencyto succeed in a rapidly changing landscape driven by innovation and global challenges.This shift necessitates a focus on continuous learning and development to ensure workersremain relevant in the evolving industrial landscape.The demand for technical skills suchas programming,data analysis,and experience with emerging technologies like AR,VR,and XR is likely to increase 95.IR5.0 also emphasizes human-centricity and highlights theimportance of soft skills such as creativity,critical thinking,and emotional intelligence 96.Organizations are increasingly recognizing the need to invest in their existing work-force through upskilling and reskilling programs rather than solely relying on hiring newtalent 97.To address these needs,companies and educational institutions have been de-veloping new training paradigms.Also,a systematic approach to workforce developmentthat considers the interrelated challenges of skill shortages and technological advancementsis essential 98.This could involve partnerships with educational institutions to developcurricula that are responsive to industry demands.Furthermore,training programs andbootcamps can help individuals and organizations identify skill gaps and tailor traininginitiatives accordingly 99.With advancements in AI,robotics,and digital tools reshapingindustries,employers now expect employees to be proficient in using technology.Theneed for knowledge in AI,cybersecurity,and automation tools is skyrocketing.Someargue that due to the recent surge in AI capabilities,there may be job cuts,which is notfalse.However,to keep pace with market needs and remain relevant,people should notfear being replaced by AI.Instead,we should focus on exploring how to leverage AI togenerate more employment opportunities and enhance its effective utilization.Therefore,competition lies not with AI,but with those who know how to effectively and efficientlyuse AI to automate and augment their skills.Moreover,the use of AR and VR for immer-sive learning experiences,alongside on-the-job training and microlearning modules forskill development,is increasing 100.The intent is to create a workforce that is not onlytechnically proficient but also adaptable and innovative,capable of working alongsideadvanced technologies while providing uniquely human insights and problem-solvingabilities 93.5.2.ErgonomicsIR5.0 brings a renewed focus on ergonomics,particularly in the context of humanmachine collaboration.With the growing integration of IoT,data-centric work,and remotesetups,working from home has become more popular nowadays.Moreover,during theCOVID-19 pandemic,the world saw a significant shift in workplaces from office settingsto home-based environments,which accelerated the adoption of human-centric practicesin various industries.In addition,as workplaces become more technologically advanced,there is a growing need to design environments that enhance both human well-beingand productivity 101.To keep up with that,physical ergonomics in IR5.0 are changingdue to the integration of smart technologies.Sensor-based systems and wearable devicesare being used to monitor and analyze workers movements and postures in real-time,enabling personalized ergonomic interventions 102.This approach not only helps inpreventing musculoskeletal disorders but also contributes to increased productivity andjob satisfaction.Similarly,cognitive ergonomics is gaining prominence as IR5.0 emphasizesthe importance of human-centric design in complex technological environments.ResearchMachines 2025,13,26716 of 34indicates that well-designed humanmachine interfaces can significantly reduce cognitiveload and improve decision-making processes 103.For instance,the use of AR in indus-trial settings has shown promises in enhancing worker performance and reducing bothmental and physical strain.The transition also introduces the concept of collaborativerobots,which are designed to work alongside humans safely.This creates a new needfor ergonomic design that considers the physical and cognitive interactions between hu-mans androbots 95,104,105.The rapid technological changes and new work paradigmsassociated with this transition can also impact employees psychological states 106.Re-search indicates that organizations implementing mental health initiatives as part of theirIR5.0 transition strategies see improvements in innovation,productivity,and employeesatisfaction 107.6.Tools and TechniquesAs discussed in Sections 2 and 3,IR4.0 primarily focuses on integrating cyber-physicalsystems,IoT,and advanced data analytics to optimize efficiency.In contrast,IR5.0 movestoward a more human-centric model,focusing on collaboration between humans and ma-chines while addressing ergonomics,mental health,sustainability,and resilience.Drivingthis shift are key tools and techniques that build upon the automation-oriented paradigmsof IR4.0 and human well-being and ecological balance.This section highlights these piv-otal tools,some of which are inherited from earlier industrial transformations and othersnewly emerging.6.1.Data DecentralizationTraditional centralized data storage systems are vulnerable to cyber threats and singlepoints of failure,so decentralized systems distributed across multiple nodes are essential.This decentralized approach is made possible by advancements in edge computing andblockchain-based multi-node decentralized methods.Edge ComputingEdge computing enables us to process data locally with enhanced security and re-duced latency.Its primary role is faster data analytics and automation through IoT,whichbenefits the industry through real-time quality control and predictive maintenance.Thisapproach supported IIoT applications by facilitating quick and localized decision-making.For example,traditional cloud models often struggle with delays and bandwidth costsin latency-critical scenarios such as self-driving vehicles and autonomous robots 108.Furthermore,efficient resource utilization through edge computing is another major advan-tage,as it optimizes network resource usage by processing time-sensitive data on-site andsending only relevant information to the cloud 109.As we transition from IR4.0 to IR5.0,edge computing architectures evolve from focusing solely on operational efficiencies to em-phasizing more human-centric and sustainable outcomes,such as personalized processesand reduced carbon footprints through localized data processing.For example,in smartmanufacturing environments,edge AI can dynamically adjust robotic assistance based onworkers physical strain levels and thus promote ergonomic safety and well-being.Further-more,inthecontextofsustainability,edgecomputingprioritizesenergy-efficientprocessing,reducing carbon footprints by minimizing unnecessary data transmission and optimizingresource consumption locally.Another key transformation is the shift towards federatedlearning and distributed intelligence at the edge,which ensures privacy-preserving AIapplications.This is particularly relevant in human-centric environments where sensitivebiometric or operational data needs to be processed locally rather than transmitted tocentralized servers.Machines 2025,13,26717 of 34BlockchainBlockchain technology could complement edge computing by providing a secure,transparent,and decentralized data-sharing mechanism,which is one of the focal areas inIR5.0.Blockchain is an immutable,shared ledger that guarantees data integrity,traceability,and trust in complex industrial systems.With the help of a blockchain-based edge comput-ing framework,it is possible to eliminate the concept of a single trusted entity and ensurethat every time a user or server wants to enter the system,the authentication process iscarried out over the network automatically,as depicted in Figure 4 110.This technology isimportant for merging advanced technologies such as AI,IoT,and cyber-physical systems.In the context of IR5.0 humanmachine collaboration,blockchain supports secure datasharing,smart contracts,and decentralized decision-making.These features boost opera-tional efficiency and reduce the dependency on centralized authorities.For example,smartcontracts facilitate payments and information exchanges among manufacturers,suppliers,and clients and promote horizontal integration across the value chain 111.On the otherhand,in IR5.0,blockchains role extends beyond transparency to enable human-centricconnections and ethical operations.For instance,blockchain-powered Unified Names-pace systems securely integrate data from IoT devices and sensors 112;it creates verticalconnections between humans,machines,and systems and ensures traceable and trustedcommunications across different levels of the chain.Aligned with the benefits of edge com-puting,blockchain also promotes sustainability and resilience by reducing inefficienciesand disruption cases from various stakeholders 113.Machines 2025,13,x FOR PEER REVIEW 17 of 35 computing framework,it is possible to eliminate the concept of a single trusted entity and ensure that every time a user or server wants to enter the system,the authentication pro-cess is carried out over the network automatically,as depicted in Figure 4 110.This tech-nology is important for merging advanced technologies such as AI,IoT,and cyber-phys-ical systems.In the context of IR5.0 humanmachine collaboration,blockchain supports secure data sharing,smart contracts,and decentralized decision-making.These features boost operational efficiency and reduce the dependency on centralized authorities.For example,smart contracts facilitate payments and information exchanges among manufac-turers,suppliers,and clients and promote horizontal integration across the value chain 111.On the other hand,in IR5.0,blockchains role extends beyond transparency to ena-ble human-centric connections and ethical operations.For instance,blockchain-powered Unified Namespace systems securely integrate data from IoT devices and sensors 112;it creates vertical connections between humans,machines,and systems and ensures tracea-ble and trusted communications across different levels of the chain.Aligned with the ben-efits of edge computing,blockchain also promotes sustainability and resilience by reduc-ing inefficiencies and disruption cases from various stakeholders 113.Figure 4.Privacy-aware secured edge computing framework using blockchain 110.6.2.HumanMachine Collaboration Humanmachine collaboration is at the heart of IR 5.0.Todays industry seeks seam-less collaboration between workers and advanced systems,shifting from task-based au-tomation to a more holistic approach that prioritizes ergonomics,safety,and user.A fu-ture is emerging in which repetitive,high-risk,and data-intensive tasks can be delegated to robots,enabling humans to concentrate on strategic thinking,innovation,and problem-solving.Additionally,instead of merely replacing human tasks,the goal is to foster greater trust in AI-driven processes while addressing user needs for enhanced satisfaction Figure 4.Privacy-aware secured edge computing framework using blockchain 110.6.2.HumanMachine CollaborationHumanmachinecollaborationisattheheartofIR5.0.Todaysindustryseeksseamlesscollaboration between workers and advanced systems,shifting from task-based automationto a more holistic approach that prioritizes ergonomics,safety,and user.A future isemerging in which repetitive,high-risk,and data-intensive tasks can be delegated to robots,Machines 2025,13,26718 of 34enabling humans to concentrate on strategic thinking,innovation,and problem-solving.Additionally,instead of merely replacing human tasks,the goal is to foster greater trust inAI-driven processes while addressing user needs for enhanced satisfaction and well-being.Co-bots,humanoids,wearable technologies,and immersive AR,VR,and XR technologiesare a few of the tools guiding us in that direction.Collaborative Robots(Co-bots)and HumanoidsCo-bots are one important shift toward humanmachine synergy,and they enableworkers and robots to share tasks in proximity without extensive safety barriers.In contrastto traditional“caged”industrial robots,co-bots integrate advanced sensors,force-limitingjoints,and intuitive programming interfaces.From the industrial example,it is quiteevident that this type of robotic system is boosting the emergence of IR5.0 through a morehuman-centric workspace.Nowadays,co-bots work side by side with humans in industrialsettings.For example,in BMWs Spartanburg plant,co-bots assist in door assemblyby precise rolling and insulation onto car doors,which is an ergonomically demandingprocess for human workers 114.This benefits the workers by preventing them fromperforming repetitive handling processes and reducing their skeletal strain with ensuredconsistent assembly quality.It is also an example of effective humanrobot teaming inwhich human workers perform the decision-making tasks,and the robots conduct thetasks that require consistency,force,and precision.Ford introduced“Robbie the Co-bot”,specifically designed to help an employee with wrist and shoulder issues in attachingcovers to engine blocks 115.By taking over the force-intensive part of the process,the co-bot significantly reduces physical strain while allowing the operator to maintainoversight and control.The recent AI boom has also witnessed significant advancementsin humanoid robot research.Unlike co-bots,which are designed for collaboration withhumans,humanoid robots closely resemble humans and utilize advanced AI capabilities.They are primarily trained using reinforcement learning to adapt to human actions andare being studied for complex task scenarios.Some researchers are exploring teleoperatinghumanoids,where a human operator controls a robot from a distance.The primary focushere is on executing tasks that demand high precision at a remote site 116.This type ofresearch and application will make the future of industry and workplaces more inclusive,ensure broader workforce participation,and reduce the risk of injuries.6.3.Wearable and Immersive TechnologiesWearable DevicesWearable devices such as virtual gloves,head-mounted displays,exoskeletons,smartsafety vests,and braincomputer interfaces(BCIs)play a crucial role in the IR5.0 transitionby enhancing operator safety,ergonomics,and adaptability.Exoskeletons,such as EksoBionics Ekso EVO,help redistribute load during repetitive overhead tasks,mitigating in-jury risks and enabling workers with physical limitations to remain active 117.Sanofi,forinstance,employed co-bots fitted with wearable sensors for product packaging,illustratinghow integrated solutions reduce strain and boost productivity 118.Similarly,BCIs continuously capture an operators cognitive and emotional statesfrom neural activity monitoring.The primary purpose of BCIs is to interact with theenvironment using brain signals without any motor activity,which in its finest form can becalled telepathy.Apart from various invasive and non-invasive methods,the basic methodincludes collecting neural signals and translating them into commands using complexmachine learning(as shown in Figure 5)119.This technology has immense potential inthe IR5.0 perspective within the fields of communication,control,healthcare,and gam-ing.Researchers around the world are making significant progress with the advent of AIproliferation and algorithmic advancements in this area.Recently,Neuralink 120 andMachines 2025,13,26719 of 34BCI Neural Electronic Opportunity(NEO)121 achieved some significant breakthroughsby completing the first wireless and implantable BCI clinical human trials independently.The role of active BCI applications goes beyond just monitoring,as they enable intuitivelycontrol machines and robots.Because of this control advantage,BCIs can enable a collab-orative environment where operators can manage devices through their thoughts.Thisdevelopment promises to improve operational efficiency and make industrial processesmore accessible to those with physical limitations 122.Machines 2025,13,x FOR PEER REVIEW 19 of 35 enable intuitively control machines and robots.Because of this control advantage,BCIs can enable a collaborative environment where operators can manage devices through their thoughts.This development promises to improve operational efficiency and make industrial processes more accessible to those with physical limitations 122.Figure 5.Methodologies and technical approaches of BCIs(upper)and recent reports from US and Chinese teams employed invasive methods(lower).Telepathy used invasive design while NEO used semi-invasive 119.On the other hand,in contrast to active BCI,passive BCI interprets brain activity without the users conscious effort to control it,essentially“listening”to the brains neural signals to understand cognitive states such as emotions or attention levels.By monitoring workload,stress,or diminished vigilance,this type of BCI enables real-time interventions,such as shifting tasks,suggesting breaks,or adapting training difficulty.They also open new possibilities for hands-free robot control and more accessible work environments 123.Passive BCIs can monitor an operators mental states,such as fatigue or cognitive overload,allowing for real-time interventions to prevent errors and improve overall workplace safety 124.Additionally,BCIs contribute to personalized training and skill development in industrial settings.By adapting training programs based on real-time cog-nitive assessments,these technologies can optimize learning processes and prevent frus-tration among workers 123.This personalized approach aligns with the core principles of IR5.0,which values adaptability and human well-being over rigid automation proto-cols.Their integration into industrial environments represents a significant shift from the technology-driven focus of IR4.0 to a more inclusive and adaptive paradigm that priori-tizes human factors in manufacturing processes 125.Immersive Technologies From the early command line interface to graphical user interfaces,to smartphones,and now to modern immersive technologies,the ways in which humans interact with technology have been continuously redefined throughout history 126(see Figure 6).The latest generation of user interfaces,driven by advancements in spatial computing,is now commercialized through AR,VR,and MR products,enabling more seamless and intuitive interactions between humans and machines 127.Spatial computing bridges the digital and physical worlds,integrating VR,AR,or even AI-powered MR to create real-time,adaptive environments.With capabilities such as spatial mapping,sensory integration,and computer vision,immersive technologies are evolving beyond static visualization tools into interactive,human-centric systems to facilitate collaboration,cognition,and de-cision-making.During IR4.0,the primary focus of VR and AR systems was on Figure 5.Methodologies and technical approaches of BCIs(upper)and recent reports from US andChinese teams employed invasive methods(lower).Telepathy used invasive design while NEO usedsemi-invasive 119.On the other hand,in contrast to active BCI,passive BCI interprets brain activitywithout the users conscious effort to control it,essentially“listening”to the brains neuralsignals to understand cognitive states such as emotions or attention levels.By monitoringworkload,stress,or diminished vigilance,this type of BCI enables real-time interventions,such as shifting tasks,suggesting breaks,or adapting training difficulty.They also opennew possibilities for hands-free robot control and more accessible work environments 123.Passive BCIs can monitor an operators mental states,such as fatigue or cognitive over-load,allowing for real-time interventions to prevent errors and improve overall workplacesafety 124.Additionally,BCIs contribute to personalized training and skill developmentin industrial settings.By adapting training programs based on real-time cognitive assess-ments,these technologies can optimize learning processes and prevent frustration amongworkers 123.This personalized approach aligns with the core principles of IR5.0,whichvalues adaptability and human well-being over rigid automation protocols.Their integra-tion into industrial environments represents a significant shift from the technology-drivenfocus of IR4.0 to a more inclusive and adaptive paradigm that prioritizes human factors inmanufacturing processes 125.Immersive TechnologiesFrom the early command line interface to graphical user interfaces,to smartphones,and now to modern immersive technologies,the ways in which humans interact withtechnology have been continuously redefined throughout history 126(see Figure 6).Thelatest generation of user interfaces,driven by advancements in spatial computing,is nowcommercialized through AR,VR,and MR products,enabling more seamless and intuitiveinteractions between humans and machines 127.Spatial computing bridges the digitaland physical worlds,integrating VR,AR,or even AI-powered MR to create real-time,Machines 2025,13,26720 of 34adaptive environments.With capabilities such as spatial mapping,sensory integration,andcomputer vision,immersive technologies are evolving beyond static visualization toolsinto interactive,human-centric systems to facilitate collaboration,cognition,and decision-making.During IR4.0,the primary focus of VR and AR systems was on visualization andefficiency in gaming and training.However,as IR5.0 shifts toward deeper humanmachinecollaboration,technologies embedding affective computing to interpret cognitive andemotional states emerge.Immersive tools are no longer just static interfaces but adaptiveinstruments that respond dynamically to user engagement and environmental factors,offering multifaceted use cases 128.This transition is particularly evident in industrialand manufacturing applications.Modern AR and VR systems are now used not only fordesign,training,and maintenance but also for real-time decision support.Such solutionsexpedite“virtual commissioning”,allowing companies to simulate and optimize factorylayouts before physical implementation.As IR5.0 unfolds,XR is now integrated intolive operations;it not only supports training and remote assistance but also collaborativedesign sessions,and it empowers front-line operators with context-specific insights 129.Going beyond,AR headsets bring context-specific,key data into the operators field ofview.In DHL warehouses,these wearables have reportedly improved picking processesby 250.Likewise,smart safety vests Elokon offers use real-time tracking to slow orhalt machinery when workers enter hazardous zones 117.Additionally,XR solutionsnow offer real-time visual cues,interactive work instructions,and remote expert guidance,ensuring that technology adapts to humans rather than the other way around.The shiftfrom passive visualization to dynamic humanmachine collaboration marks a definingcharacteristic of the IR5.0 revolution.Machines 2025,13,x FOR PEER REVIEW 20 of 35 visualization and efficiency in gaming and training.However,as IR5.0 shifts toward deeper humanmachine collaboration,technologies embedding affective computing to in-terpret cognitive and emotional states emerge.Immersive tools are no longer just static interfaces but adaptive instruments that respond dynamically to user engagement and environmental factors,offering multifaceted use cases 128.This transition is particularly evident in industrial and manufacturing applications.Modern AR and VR systems are now used not only for design,training,and maintenance but also for real-time decision support.Such solutions expedite“virtual commissioning”,allowing companies to simu-late and optimize factory layouts before physical implementation.As IR5.0 unfolds,XR is now integrated into live operations;it not only supports training and remote assistance but also collaborative design sessions,and it empowers front-line operators with context-specific insights 129.Going beyond,AR headsets bring context-specific,key data into the operators field of view.In DHL warehouses,these wearables have reportedly im-proved picking processes by 250.Likewise,smart safety vests Elokon offers use real-time tracking to slow or halt machinery when workers enter hazardous zones 117.Ad-ditionally,XR solutions now offer real-time visual cues,interactive work instructions,and remote expert guidance,ensuring that technology adapts to humans rather than the other way around.The shift from passive visualization to dynamic humanmachine collabora-tion marks a defining characteristic of the IR5.0 revolution.Figure 6.Timeline of user interfaces 126.6.4.Generative AI Recent advancements in AI,particularly in the form of generative AI and autono-mous agents,are accelerating the transition to Industry 5.0,where human-centric and in-telligent automation plays a pivotal role.Unlike traditional AI systems,which primarily relied on deterministic rule-based logic or statistical learning for automation,modern gen-erative AI models leverage deep learning architectures,particularly transformer networks and generative adversarial networks(GANs),to generate novel solutions beyond simple Figure 6.Timeline of user interfaces 126.6.4.Generative AIRecent advancements in AI,particularly in the form of generative AI and autonomousagents,are accelerating the transition to Industry 5.0,where human-centric and intelligentMachines 2025,13,26721 of 34automation plays a pivotal role.Unlike traditional AI systems,which primarily reliedon deterministic rule-based logic or statistical learning for automation,modern genera-tive AI models leverage deep learning architectures,particularly transformer networksand generative adversarial networks(GANs),to generate novel solutions beyond sim-ple pattern recognition.While earlier AI generations were designed mainly to automatelarge-scale,repetitive,and predefined tasks,generative AI introduces creative and adaptivecapabilities such as autonomous design,advanced process optimization,and real-timedecision-making support.These advancements enable a new paradigm of AI-human col-laboration,where AI augments human expertise rather than only executing predefinedrules 131.This transformation holds great promise for industries;in adaptive manu-facturing as an example,generative AI autonomously designs innovative materials withenhanced properties,shortening development cycles and boosting product innovation.Inaddition,generative AI is also influencing healthcare by accelerating drug discovery 132,predicting complex protein structures 133,and facilitating the development of noveltherapeutics with unprecedented precision.This transition not only enhances efficiencybut also fosters deeper synergy between human and intelligent systems,which essentiallyembodies IR5.0s core principle of augmenting human creativity and decision-makingrather than just automating or replacing human roles 134.Regarding collaboration andadaptability aspects,generative AI promotes real-time collaboration and adaptability byusing agentic AI to handle complex tasks with minimal oversight.Paired with co-bots,these AI-driven systems streamline production,respond dynamically to changing needs,and provide on-the-fly digital instructions.In terms of workforce empowerment in theindustry,generative AI-based tools are also in use to evaluate employee skill sets,monitorpersonalized cognitive load,and offer customized training.This turns rigid workflows intoflexible,people-focused systems that blend human expertise with AI insights 131,135.6.5.Advanced Wireless NetworkEfficient wireless networks serve as a backbone for interconnected factories.The 5Gnetwork,characterized by low latency and high connection density,enables technologiessuch as massive IoT,AI-driven automation,and advanced AR/VR applications for trainingand remote maintenance 136.While 5G currently leads industrial connectivity,6G,quantum,and bio-inspired networks will push the limits of human-AI collaboration in thenear future.To keep pace with advancements in AI algorithms,faster data transfer,reducedlatency,and AI-native networks are required 137,138.The integration of 6G with AI andblockchain will result in self-optimizing,intelligent,industry-wide ecosystems capableof making autonomous decisions without human input.Holographic communicationand digital twins powered by 6G will be significant breakthroughs,enabling engineersand operators to engage with immersive,real-time virtual environments to test,optimize,and implement manufacturing solutions before physical deployment.Some researchersalso highlighted the potential of 6G to integrate AI-driven“semantic communications”,which will ensure that only contextually meaningful information is transmitted 139.This approach will reduce bandwidth usage and support emerging concepts like“Goal-Oriented”networking and benefit use cases in energy grid management,autonomoussystems,and immersive telepresence 140,141.7.Applications and OpportunitiesIR5.0 advancements are notably evident in various industry sectors,such as manu-facturing and production,consumer and retail,biotechnology and healthcare,and serviceindustry and infrastructure.In manufacturing,smart factories and future maintenanceimprove operational efficiency and product quality,especially in automobile,electronics,Machines 2025,13,26722 of 34and semiconductor industries.Retailers take advantage of AI and large data to providepersonalized customer experience and embrace permanent practices.Biotech industriesbenefit from the discovery of AI-powered drug and sustainable biomanufacturing,whilehealthcare sees an improvement in telemedicine and patient care.The service industriesadopt IoT and knowledge-based systems for individual and efficient operations.Thissection focuses on how these innovations are re-designed to meet the challenges and socialdemands that develop industries.7.1.Manufacturing and ProductionThe ongoing IR5.0 transition is significantly transforming the manufacturing and pro-duction sectors,particularly in the automobile,electronics,and semiconductor industries.Digital transformation is becoming increasingly evident,as manufacturers optimize pro-duction processes through real-time data analytics and automation 142,143.Additionally,the transition to smart factories,where machines interact and work together with humans,results in more efficient production lines and lower operational costs 144,145.The use ofrobotics and automation in electronics manufacturing has been shown to increase precisionand speed,which are critical in a sector that demands high levels of accuracy due to thecomplexity of electronic components 146.Additionally,the application of big data ana-lytics in understanding consumer behavior and market trends is helping manufacturersto tailor their products more effectively 147.Similarly,the semiconductor industry isalso experiencing transformative changes due to this transition.The demand for higherefficiency and lower energy consumption in semiconductor manufacturing has led to theadoption of advanced manufacturing technologies such as AI 148.For example,the useof AI and machine learning algorithms in design and production processes allows for theoptimization of chip performance while minimizing resource usage 149.7.2.Consumer and RetailThe personalization of consumer experiences is a hallmark of Industry 5.0.AI and bigdata analytics enable retailers to analyze consumer behavior and preferences in real-time,allowing for tailored marketing strategies and product recommendations 150.This capa-bility not only enhances customer satisfaction but also fosters brand loyalty as consumersare more likely to engage with brands that understand their individual needs 151.Fur-thermore,the use of AI-driven chatbots and virtual assistants in retail settings facilitatesimmediate customer service and improves the overall shopping experience 152.Moreover,the shift towards omnichannel retailing allows consumers to interact with brands acrossmultiple platforms seamlessly.This integration of online and offline channels providesconsumers with greater flexibility and convenience in their shopping experiences.Forinstance,consumers can research products online,check availability in physical stores,andmake purchases through various digital platforms 153.This omnichannel approach hasbeen particularly accelerated by the COVID-19 pandemic,which has significantly changedconsumer shopping behaviors and resulted in increased reliance on online shopping andcontactless payment methods 154,155.Sustainability is another essential aspect influenced by Industry 4.0 and 5.0.Con-sumers are increasingly aware of environmental issues and are seeking sustainable prod-ucts and practices from retailers.The integration of digital services in retail allows for bettertraceability,transparency in supply chains,and helps consumers make informed choicesabout the products they purchase 151.Retailers respond to this demand by adoptingsustainable practices,such as reducing waste and utilizing eco-friendly materials,whichnot only appeal to environmentally conscious consumers but also enhance brand reputa-tion 156.Furthermore,the COVID-19 pandemic has accelerated the adoption of digitalMachines 2025,13,26723 of 34technologies in retail and caused significant shifts in consumer behavior.Many consumershave developed new shopping habits,such as increased online purchasing and a preferencefor local products,which have been influenced by the need for safety and convenienceduring the pandemic 157.Retailers are adapting to these changes by enhancing theirdigital presence and offering innovative solutions,such as virtual shopping experiencesand enhanced delivery services,to meet evolving consumer expectations 158.7.3.Biotech and HealthcareThe application of AI and machine learning in biotechnology is accelerating drug dis-covery and enhancing precision medicine 159.By analyzing vast datasets,AI algorithmshelp researchers identify potential drug candidates more efficiently,reducing both time andcost 160,161.For instance,the application of AI in genomics allows for the rapid analysisof genetic data,facilitating personalized medicine approaches that tailor treatments toindividual patients based on their genetic profiles.The use of microfluidic technologies inbiotechnology helps us to have more efficient and accurate experimental methods.Microflu-idics enables the manipulation of small volumes of fluids,allowing for high-throughputscreening of biological samples 162.This technology is particularly beneficial in drugdevelopment and diagnostics,where it can significantly reduce the number of reagentsneeded and improve the speed of experiments.As a result,researchers can now conductmore experiments in less time and make faster discoveries inbiotechnological applications.In healthcare delivery,the implementation of telemedicine and remote monitoringsystems is enhancing patient care.These technologies allow healthcare providers to monitorremote patients health in real-time,improving the management of chronic diseases andenabling timely interventions 163.The integration of IoT devices in healthcare settingsfacilitates the collection of patient data,which can be analyzed to provide insights intohealth trends and outcomes,ultimately leading to improved patient management strategies.Furthermore,the use of wearable health technologies allows patients to take an active rolein managing their health 164.The focus on sustainability and ethical considerations inbiotechnology is also gaining momentum with the advent of IR5.0 transition.This newparadigm emphasizes the importance of human-centric approaches and environmentalsustainability in biotechnological innovations.For instance,the development of bio-basedproducts and sustainable bio-manufacturing processes is becoming increasingly relevant asindustries seek to reduce their environmental footprint 165.The integration of biotech-nological solutions in healthcare,such as the use of biodegradable materials for medicaldevices,aligns with the growing demand for sustainable practices in the sector 166.Also,the economic potential of biotechnology is being realized through the commercialization ofinnovative products and services.As biotech industries continue to grow,opportunitiesarise for startups and established companies to develop novel solutions that address press-ing health challenges 167,168.The collaboration between academia and industry is crucialin this regard,as it helps the translation of research findings into practical applications 169.7.4.Service IndustryIntegrating the IoT,cloud computing,and smart sensing and analytics technologieshas revolutionized traditional service models.For instance,in finance,the adoption ofautomated systems allows for real-time data analysis and decision-making,which improvesservice and customer satisfaction 170.Similarly,in education,smart technologies facilitatepersonalized learning experiences and meet diverse student needs more effectively 171.Furthermore,the human-centric approach of IR 5.0 is gaining more traction in the hospital-ity sector,where automation tools are increasingly utilized to enhance guest experienceswhile maintaining a human touch.For instance,AI-driven chatbots and virtual assistantsMachines 2025,13,26724 of 34can manage routine inquiries,allowing staff to engage in more meaningful interactionswith guests 172.In addition,VR-based room demos allow guests to have a high-fidelityexperience of the space even before their arrival booking.7.5.Infrastructure and UtilitiesIR5.0 transition is changing the infrastructure and utilities landscape by enhancingefficiency,promoting sustainability,and fostering resilience.Infrastructure managementrequires real-time monitoring and optimization of energy consumption to reduce opera-tional costs 173.Today,people are becoming increasingly familiar with smart meters andsensors that enable energy companies to collect data on energy usage patterns.These datacan then be analyzed to optimize energy distribution and reduce waste 174.Furthermore,the emphasis on sustainability in energy production drives the adoption of renewablesources.Similarly,the transportation sector is also experiencing significant changes due toIR5.0.The advent of autonomous vehicles,smart traffic management systems 175,andconnected infrastructure is transforming how goods and people are transported.Theseinnovations lead to reduced congestion,lower emissions,and enhanced safety 176.Forexample,the implementation of AI-driven traffic management systems can optimize trafficflow and reduce travel times,thereby improving the overall efficiency of urban transporta-tion networks 177.Also,integrating data analytics in transportation planning enablesbetter resource allocation and infrastructure development to meet future demands 178.In the construction industry,tools and technologies such as Building Information Mod-eling,drones,and 3D printing are improving the efficiency and accuracy of constructionprocesses 179.These technologies enable real-time collaboration among stakeholders,reducing delays and costs associated with traditional construction methods 180.Addi-tionally,the focus on sustainable construction practices is leading to the development ofeco-friendly materials and methods,which are essential for minimizing the environmentalimpact of construction activities 181.In utilities like water management,integrating smarttechnologies into water supply and waste management systems enables more efficient re-source management and enhances service delivery 182.For instance,smart water meterscan detect leaks and monitor consumption patterns and thus inform service providers torespond proactively to issues and optimize their operations 1838.Challenges and Future DirectionsIndustry 4.0 has introduced numerous opportunities,including enhanced efficiency,innovation,and sustainability.For instance,cutting-edge automation and AI-based pre-dictive maintenance significantly reduce operational costs by minimizing idle time.Byintegrating cyber-physical systems,IoT,AI,and big data analytics,Industry 4.0 has ischanging global manufacturing and supply chain management.Change is never withoutchallenges.As progress is made,obstacles are encountered,failures occur,and valuablelessons are learned from experience.Understanding potential problems in advance can helpus navigate difficult times more effectively.One of the most critical obstacles is the highcost of digital transformation.Industries must invest heavily in advanced technologies,infrastructure,and workforce upskilling.Additionally,as digitalized connectivity expands,the risk of cybersecurity threats intensifies,exposing industries to cyber-attacks and criticaldata leakages.Also,industries should secure interoperability among various systems,platforms,and instruments.Resistance to change among traditional industries and anuncertain regulatory environment also pose barriers to advancement.Industry 5.0 buildsupon the technological advancement of Industry 4.0 and shifts to a human-centric,sustain-able,and resilient industry paradigm.Industry 4.0 advances automation and digitalizationby integrating AI,IoT,and cyber-physical systems.Industry 5.0 emphasizes cooperationMachines 2025,13,26725 of 34between humans and smart technologies,fostering humanmachine collaboration.Yet,ensuring its ethical and sustainable implementation requires a strong focus on workforcereskilling,enhanced cybersecurity,and robust data protection strategies.By addressingthese challenges,a well-structured plan can be developed to ensure a resilient future.One of the primary challenges in cybersecurity is the increasing complexity and in-terconnectivity of todays systems.Integrating IoT devices into industrial environmentscreates numerous entry points for cyberattacks,making it difficult to secure networkseffectively 184.The rapid adoption of these technologies has led to new vulnerabilitiesthat organizations must address,particularly where interconnected systems are prevalent.Moreover,the reliance on cloud computing and data sharing further complicates securitymeasures,as organizations must ensure that data are protected across various platformsand environments 185.Another significant challenge is the skills gap in the cybersecurityworkforce.The demand for qualified cybersecurity professionals has surged due to theincreasing frequency and sophistication of cyber threats 186.Developing comprehen-sive cybersecurity frameworks that include training and certification programs is crucialfor addressing this skil
2025-04-01
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Clarity Redefined:The Rise of Lab Grown Diamonds2Lab-grown diamonds make up 30%of the global diamond.
2025-03-27
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RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|1GaN Reliability and Lifetime Projections:Phase 17The rapid adoption of Gallium Nitride(GaN)devices in many diverse applications calls for continued accumulation of reliability statistics and research into the fundamental physics of failure in GaN devices,including integrated circuits(ICs).This Phase 17 Reliability Report presents ongoing efforts using test-to-fail methodology to develop more comprehensive and advanced lifetime models,which is aimed at accurately projecting the reliability of GaN devices under more complex mission-specific operating conditions.Siddhesh Gajare,Ph.D.,Duanhui Li,Ph.D.,Ricardo Garcia,Angel Espinoza,Jordan Green,Peter Tieu,Ph.D.,Christopher Wong,Anthony Nguyen,David Wu,Shengke Zhang,Ph.D.HERE ARE THE NEW ADDITIONS TO THE PHASE 17 RELIABILITY REPORTThe latest Phase 17 reliability report further expands the first-principles lifetime models to address more complex operating conditions,enabling more accurate lifetime projections for mission specific applications.Additionally,the latest version focuses on presenting the complex physics-based models in a variety of application-driven,user-friendly formats,allowing readers to quickly comprehend the concepts and apply them to practical use conditions with ease.Section 4.1 presents an expanded gate lifetime model which now incorporates the effect of gate leakage current under various gate-source voltages and temperatures into the dominant impact ionization mechanism.Next,a duty cycle-based repetitive transient gate overvoltage rating at 7 V was developed and validated through the development of a repetitive inductive-switching gate overvoltage testing system,which accurately models the resonance-like transient gate overvoltage stress during applications.Section 4.2 provides more testing validation to the repetitive transient drain overvoltage specification,which shows the excellent robustness of GaN devices under drain-source overvoltage conditions.Section 4.3.5 is a new section that presents the latest development and measurements to quantify the pulsed current rating for the generation-6 and generation-5 GaN devices at various gate drive voltages and temperatures.The testing was also extended to more than 100 million pulses,after which minimal parametric shifts were observed.This work paves the way for GaN in applications that require transient high current pulses,such as light detection and ranging(lidar).Section 4.4 presents the development of a comprehensive lifetime model for thermomechanical stress,applicable to both temperature cycling(TC)stress and power cycling(PC)stress.This revamped section further enhances the completeness of the TC lifetime modeling by incorporating die dimensions,bump shape,TC test conditions,ramp rate,and PCB properties and PCB thickness.Additionally,this section extends the thermomechanical discussions and lifetime modeling to power cycling for the first time.In PC,the temperature rise results from device self-heating during power-on,while the PCB temperature lags behind,creating a non-uniform thermal gradient from the device to the PCB.Section 4.4.4 discusses and models the critical parameters involved in PC stress,including cycling time,internal die dimensions and geometry within a PQFN package,and temperature variation between two extremes.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|2TABLE OF CONTENTS SECTION 1:NEW FOCUS AND ADDITIONS OF THE PHASE 17 RELIABILITY REPORT.4SECTION 2:DETERMINING WEAR-OUT MECHANISMS USING TEST-TO-FAIL METHODOLOGY.4SECTION 3:USING TEST-TO-FAIL RESULTS TO PREDICT DEVICE LIFETIME IN A SYSTEM.5SECTION 4:WEAR-OUT MECHANISMS.5 4.1 Gate Wear-Out.5 4.1.1 Introduction to the Reliability of Schottky-type pGaN Gates.5 4.1.2.Development of a Comprehensive Gate Reliability Lifetime Model.6 4.1.3.Development of Repetitive Transient Gate Overvoltage Specification.8 4.2 Drain Wear-Out.10 4.2.1.Introduction to Drain Wear-Out Mechanisms.10 4.2.2.Development of a Physics-Based Lifetime Models for Dynamic RDS(on).10 4.2.3.Impact of Higher Drain-source Voltage Stress.12 4.2.4.Development of Repetitive Transient Drain Overvoltage Specification.13 4.3.Current Density Wear-Out.15 4.3.1.Introduction to Current Density Wear-out Mechanisms.15 4.3.2.Safe Operating Area.15 4.3.3.Short-Circuit Robustness.17 4.3.4.Development of Continuous Current Rating for PQFN GaN HEMTs.19 4.3.5.Development of Pulsed Current Rating for GaN HEMTs.20 4.4 Thermomechanical Wear-Out.21 4.4.1.Introduction to Thermomechanical Wear-Out Mechanisms.21 4.4.2.Development of a Comprehensive TC Lifetime Model.21 4.4.3.Criteria for Choosing a Suitable Underfill.26 4.4.4.Development of a Lifetime Model for Power Cycling(PC)Stress.28 4.4.5.Conclusion.32 4.5.Mechanical Stress Wear-Out.33 4.5.1.Introduction to Mechanical Stress Wear-out Mechanisms.33 4.5.2.Die Shear Test of Chip-Scale Parts.33 4.5.3.Backside Pressure Test of Chip-Scale Parts.33 4.5.4.Bending Force Test of Chip-Scale Parts.33 4.5.5.Bending Test on PQFN Parts.34SECTION 5:MISSION-SPECIFIC RELIABILITY PREDICTIONS.34 5.1.Solar Application Specific Reliability.35 5.1.1.Introduction.35 5.1.2.Trends In Photovoltaic Power Conversion.35 5.1.3.Applying Test-to-Fail for Solar.35 5.1.4.Gate Bias.36 5.1.5.Drain Bias .36 51.6.Temperature Cycling.37 5.1.7.Conclusions.38 5.2.DC-DC Application Specific Reliability.38 5.2.1.Introduction.38 5.2.2.Test-to-Fail Methodology .38RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|3 5.2.3.Gate Bias.38 5.2.4.Drain Bias.38 5.2.5.Temperature Cycling.41 5.2.6.Conclusions.41 5.3.Lidar Application Reliability.42 5.3.1.Introduction to Lidar Reliability.42 5.3.2.Long-Term Stability Under High Current Pulses.42 5.3.3.Monolithic GaN-on-Si Laser Driver ICs.43 5.3.4.Key Stressors of eToF Laser Driver IC for Lidar Application.43 5.3.5.Effect of VDD,Logic Supply Voltage.43 5.3.6.Effect of VD,Laser Drive Voltage.46 5.3.7.Effect of Operating Frequency.476.SUMMARY AND CONCLUSIONS.487.References.48RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|4SECTION 1.NEW FOCUS AND ADDITIONS OF THE PHASE 17 RELIABILITY REPORT Compared to the previous Phase 16 reliability report 1,the latest version focuses on further expanding the previously developed physics-based lifetime models to encompass more complex operating conditions.This expansion includes the lifetime models for gate voltage stress,temperature cycling thermo-mechanical stress,and power cycling thermo-mechanical stress.The development of more comprehensive lifetime models leads to more accurate lifetime projections for mission specific operating conditions.The second highlight of the Phase 17 reliability report is the translation of the complex lifetime models into a variety of user-friendly formats,allowing readers to easily apply the models to practical use conditions.Additionally,this effort allows us to update our transient overvoltage rating and pulsed current specifications in the datasheets,making them more application-oriented and competitive compared to other GaN manufacturers.The results also demonstrate the excellent robustness of EPCs GaN devices.SECTION 2.DETERMINING WEAR-OUT MECHANISMS USING TEST-TO-FAIL METHODOLOGYStandard qualification testing for semiconductors typically involves stressing devices at or near the limits specified in their datasheets for a prolonged period,or for a certain number of cycles.The goal of standard qualification testing is to have zero failures out of a relatively large group of parts tested.This type of qualification testing is inadequate since it only reports parts that passed a very specific test condition.By testing parts to the point of failure,an understanding of the amount of margin between the datasheet limits can be developed,and more importantly,an understanding of the intrinsic failure mechanisms can be found.By knowing the intrinsic failure mechanisms,the root cause of failure,and the behavior of this mechanism over time,temperature,electrical or mechanical stress,the safe operating life of a product can be determined over a more general set of operating conditions(For an excellent description of test-to-fail methodology for testing semiconductor devices,see reference 2).As with all power transistors,the key stress conditions involve voltage,current,temperature,and humidity,as well as various mechanical stresses.There are,however,many ways of applying these stress-conditions.For example,voltage stress on a GaN transistor can be applied from the gate terminal to the source terminal(VGS),as well as from the drain terminal to the source terminal(VDS).For example,these stresses can be applied continuously as a DC bias,they can be cycled on-and-off,or they can be applied as high-speed pulses.Current stress can be applied as a continuous DC current,or as a pulsed current.Thermal stress can be applied continuously by operating devices at a predetermined temperature extreme for a period of time,or temperature can be cycled in a variety of ways.By stressing devices with each of these conditions to the point of generating a significant number of failures,an understanding of the primary intrinsic failure mechanisms for the devices under test can be determined.To generate failures in a reasonable amount of time,the stress conditions typically need to significantly exceed the datasheet limits of the product.Care needs to be taken to make certain the excess stress condition does not induce a failure mechanism that would never be encountered during normal operation.To make certain that excess stress conditions did not cause the failure,the failed parts need to be carefully analyzed to determine the root cause of their failure.Only by verifying the root cause can a complete understanding of the behavior of a device under a wide range of stress conditions be developed.As the intrinsic failure modes in eGaN devices are better understood,two facts have become clear;(1)eGaN devices are more robust than Si-based MOSFETs,and(2)silicon MOSFET intrinsic failure models do not generally apply when predicting eGaN device lifetime under extreme or long-term electrical stress conditions.Table 2-1 lists in the left-hand column all the various stressors to which a transistor can be subjected during assembly or operation.Using the various test methods listed in the third column from the left,and taking devices to the point of failure,the intrinsic wear-out mechanisms can be discovered.The wear-out mechanisms confirmed as of this writing are shown in the column on the right.StressorDevice/PackageTest MethodIntrinsic Failure MechanismVoltageDeviceHTGBDielectric failure(TDDB)Threshold shiftHTRBThreshold shiftRDS(on)shiftESDDielectric ruptureCurrentDeviceDC Current(EM)ElectromigrationThermomigrationCurrent Voltage(Power)DeviceSOAThermal RunawayShort CircuitThermal RunawayVoltage Rising/FallingDeviceHard-switching ReliabilityRDS(on)shiftCurrent Rising/FallingDevicePulsed Current (Lidar reliability)None foundTemperaturePackageHTSNone foundHumidityPackageMSL1None foundH3TRBNone foundACNone foundSolderabilitySolder corrosionuHASTDenrite Formation/CorrosionMechanical/Thermo-mechanical PackageTCSolder FatigueIOLSolder FatigueBending Force TestDelaminationBending Force TestSolder StrengthBending Force TestPiezoelectric EffectsDie shearSolder StrengthPackage forceFilm CrackingRadiationDeviceGamma RadiationNone foundNeutron RadiationNone foundHeavy Ion BombardmentCrystal displacement damage and ionization damageTable 2-1:Stress conditions and intrinsic wear-out mechanisms for GaN transistorsRELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|5SECTION 3:USING TEST-TO-FAIL RESULTS TO PREDICT DEVICE LIFETIME IN A SYSTEMWhen multiple failure mechanisms or stressors are involved,the total failure rate of a system,commonly known as Failure in Time(FIT),is the sum of the failure rates per failure mechanism 3,4 as shown below,where FIT is failure in time,which typically represents the number of failures in 109(1 billion)device hours,and the subscript indicates the different failure mechanisms identified.FIT is inversely proportional to mean time to failure(MTTF)as described byTherefore,by plugging Equation 3-2 into Equation 3-1,the total MTTF can be described by Equation 3-3,The subscripts are assigned to the reliability stressors that are relevant to the application of interest.Based on Equation 2-3,it is noted that the smallest denominator yields the smallest MTTF and therefore dominates the overall lifetime.It is critical to understand which stressor is the limiting factor in reliability because the weakest link warrants the most consideration during design and operations.In most applications,devices experience various stress conditions over the course of the entire mission lifespan,including a combination of different bias conditions and different temperature profiles.Each stress condition corresponds to a specific failure rate(failures per unit time),specified as FRa,FRb,FRn.The respective duration of each stress condition is denoted as ta,tb,tn.Assuming ttotal=ta tb . tn is 109 hours,the FIT calculation of total number of failures is then generalized for specific reliability stress conditions asThe time-averaged failure rate FR can be calculated as which can be simplified by introducing fractional operation time,noted as a,b,n.The sum of a,b,n is 100%which is given in Equation 3-7.Now Equation 3-5 can be simplified to It is known that the failure rate under each sub-stress condition is inversely proportional to the device lifetime LT 4 when the same number of failures is generated.The relation is shown in Equation 3-9.Eq.3-1Eq.3-210Eq.3-3TotalMTTFMTTFMTTFMTTFEq.3-4Eq.3-5Eq.3-6tnn=Eq.3-7100%Eq.3-8Eq.3-9Plugging Equation 3-9 into Equation 3-8 yields Equation 3-10.where LTTotal is the total projected lifetime and LTi is the projected lifetime for each stress condition.Equation 3-10 captures how a mission profile consisting of more than one stress condition results in a system lifetime.The fractional operation time(a,b,n)in the numerators account for the times spent in harsh,moderate,and mild stress conditions.SECTION 4:WEAR-OUT MECHANISMS 4.1.Gate Wear-Out 4.1.1.Introduction to the Reliability of Schottky-type pGaN GatesSchottky-type pGaN gates are the most widely used gate structure for commercial enhancement-mode GaN HEMTs that are currently in volume production.A Schottky-type pGaN gate typically consists of gate electrode made of titanium nitride(TiN)and a pGaN gate layer that is doped with Mg.Due to the significant structural differences in gate construction between GaN HEMTs and Si-based MOSFETs,the stability and robustness of pGaN gates are of great interest to the users.In this section,after understanding the fundamental gate wear-out mechanism through test-to-fail,a physics-based gate lifetime model was developed from first principles.The model predicts a failure rate of less than one part per million(1-ppm)if the gate bias is kept below 6 VGS,Max throughout the entire mission lifespan of 25 years.The projected result is also consistent with EPCs field experience.In this new phase 17 reliability report,the gate lifetime model has been further expanded to include the voltage and temperature dependence of the electron injection current density by analyzing the gate leakage current conduction mechanisms.This inclusion enables the accurate modeling of the activation energy of mean-time-to-fail(MTTF)at various temperatures.Another common reliability question regarding Schottky-type pGaN gates is the transient overvoltage capability and robustness,due to the relatively small margin between the recommended gate drive voltage(5 V)and the datasheet maximum specification(VGS,Max=6 V).The latest phase 17 reliability report developed a 7 V repetitive transient overvoltage rating with 1%duty cycle factor,Eq.3-10RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|6DielectricGate Metalp-GaN GateMetal 1 Field PlateSourceFigure 4-1.Scanning electron microscopy(SEM)image of a gate failure.Dielectric breakdown is observed between the gate metal and the field plate metal.which was later validated through the development of a repetitive inductive switching testing circuit.4.1.2.Development of a Comprehensive Gate Reliability Lifetime ModelTo understand the gate wear-out mechanisms,accelerated time-dependent reliability testing was conducted on various EPCs GaN HEMTs at various voltages and temperatures.Failure analysis revealed that the breakdown of the silicon nitride dielectric layer,located between the gate corner and metal field plate,is primarily responsible for the pGaN gate failures,as shown in Figure 4-1.Impact ionization was identified as the main wear-out mechanism responsible for the silicon nitride dielectric breakdown failure mode 5.A four-step process was developed to explain the failure mode shown in Figure 4-1.The electron injection from the 2-dimensional electron gas(2DEG)and the subsequent acceleration within the pGaN gate layer is the first step.When the pGaN gate is subjected to a high forward gate bias(VGS),the 2DEG electrons fully populate the channel and may spill over the“bending”conduction band of the AlGaN barrier layer.Subsequently,the injected electrons are accelerated within the depleted pGaN gate layer under high forward VGS,gaining significant kinetic energy 5.When the energetic moving electrons are stopped by the TiN gate metal/pGaN interface,the resulting bombardment causes impact ionization and triggers electron-hole multiplication,which has been confirmed by luminescence measurements 6.Thus,Impact ionization and electron-hole multiplication at the TiN/pGaN interface constitute the second step.The third step involves hole accumulation within the silicon nitride dielectric layer.The positively charged holes generated by impact ionization move away from the gate electrode(under VGS)towards the metal field plate that is at ground potential during gate stress.Consequently,the holes become trapped in the silicon nitride dielectric layer,leading to an increasing positive charge density as the gate stress continues.Finally,when the trapped hole density exceeds the critical field of the silicon nitride dielectric layer,dielectric breakdown occurs,which explains the failure mode as shown in Figure 4-1.Based on the four-step impact ionization failure process,a physics-based gate lifetime model was developed from first principles.The MTTF is modeled by estimating when the trapped hole charges reach the critical charge density(QC)of the silicon nitride dielectric layer,as defined by Eq.4-1:where G is the electron-hole generation rate(s1cm3)that is denoted by Eq.4-2.It is noted that holes are the primary cause responsible for the dielectric breakdown.where Jn is the electron current density(A/cm2)that is directly proportional to the gate leakage current under forward gate bias,q is the elementary charge(coulomb=A-s),and n is the electron impact ionization coefficients(cm-1),which is defined by the Chynoweth model in Eq.4-3 7.E is the vertical electric field driven by gate bias and m is an exponent that is typically ranging from 1 to 2;an and bn are temperature dependent impact ionization coefficients that can be described by the Okuto-Crowell model 8,which are further defined by Eq.4-4 and Eq.4-5 9.where T is the temperature difference compared to 298 K in Kelvin unit 17-19.an,0=2.77 x 108 cm1,bn,0=3.20 x 107 V/cm,c=3.09 x 10-3 K1,d=5.03 x 104 K1 are the fitting parameters of impact ionization coefficients by following the Okuto-Crowell model 8.By combining Eq.4-1 Eq.4-5,the MTTF becomes Eq.4-6:First,time-dependent gate reliability testing was conducted on EPC2212 under four different gate biases:8 V,8.5 V,9 V and 9.5 V at room temperature of 25C.Therefore,T is equal to 0,leading to a simplified MTTF expression as shown in Eq.4-7.where m is 1.9,an,0=2.77 x 108 cm1,and bn,0=3.20 x 107 V/cm.Eq.4-1Eq.4-2Eq.4-3Eq.4-4Eq.4-5Eq.4-6.Eq.4-7,RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|7Figure 4-2 shows that the gate lifetime equation of Eq.4-7 provides a good fit to the measured MTTF at various gate biases.Additionally,less than 1-ppm(part per million)failure rate is predicted if the gate bias is kept at or below the maximum gate rated voltage of 6 V for 25 years.To develop a comprehensive gate lifetime model,the voltage and temperature dependence of Jn must be further investigated,where Jn is directly proportional to the forward gate leakage current(IG).Therefore,the gate leakage current in EPC2057 was measured at different temperatures and voltages,with the gate I-V results reported in 10.A significant temperature acceleration of IG is observed at higher temperatures,suggesting that thermionic emission(TE)is proposed as the dominant conduction mechanism,which can be modeled by Richardsons law 11,as shown in Eq.4-8.where A is the Richardsons constant,k is the Boltzmann constant,and B is the barrier height for electrons to overcome the AlGaN/GaN heterojunction.B is calculated to be 0.45 eV at 9.5 VGS based on the slope of the fit line shown in Figure 4-4(a).Next,time-dependent gate reliability was carried out at various temperatures with a fixed gate bias of 9.5 V on EPC2057.The Weibull distribution plot at three different temperatures(-25C,25C and 125C)is shown in Figure 4-3.When the temperature increases from-25C to 25C,the gate lifetime increases,suggesting a negative activation energy(Ea).However,as the temperature continues rising further to 125C,the gate lifetime decreases,indicating a positive Ea.This suggests that two competing effects are likely responsible for the pGaN gate breakdown failures.Figure 4-2:EPC2212 MTTF vs.VGS at 25C(and error bars)are shown for four different voltage legs.The solid line corresponds to the impact ionization lifetime model.Extrapolations of time to failure for 100 ppm,10 ppm,and 1 ppm are shown as well.Figure 4-3:Weibull distribution plots of EPC2057 under three different temperatures:-25C,25C and 125C with a fixed gate bias of 9.5 V.Figure 4-4:(a)Richardson plot from-25C to 125C with 9.5 VGS ;(b)FN plot at-25C and 25C,where the inset shows the linear fits from 9 V to 9.5 V at 25C.Time to Failure(s)Probability of Failure1001021041060.990.950.900.750.500.250.100.050.020.01-25C25C125C102010151010105100Gate Voltage(V)Time to Failure(s)56725 yearsMTTFMax Rating=6 V100 ppm10 ppm1 ppm8910Eq.4-828323640444810987650.730.951.131.38302724125C100C 75C 50C 25C 0C-25CT=35C-25C 25C9.57.56.55.5(a)(b)1/E(cm/MV)VGS(V)q/kT(V-1)InJG/E2(AV-2)InJG/T2(Acm-2 K-2)27.50.70.826.527.026.0Slope=-12.4 MV/cm1/E(cm/MV)RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|8After combining Eq.4-6 and Eq.4-8,the MTTF at higher temperatures can be written as Eq.4-9In Figure 4-4(a),the Richardson plot produces a straight line fit from 50C to 125C,which confirms that TE is the dominant conduction mechanism.However,the data points from 25C to-25C deviate from the fit line,indicating that TE is no longer the primary conduction mechanism responsible for IG.Thus,35C is projected to be the threshold temperature at which the dominant IG conduction mechanism transitions from TE to other mechanisms.When plotting ln(JG/E2)against 1/E as shown in Figure 4-4(b)for 25C and-25C,insignificant dispersion is observed when the VGS is greater than 9 V,suggesting that Fowler-Nordheim(FN)tunneling is the dominant conduction mechanism 12.The FN tunneling can be modeled by Eq.4-9.where eff is the effective barrier height,h is the Planck constant,and m*is the electron effective mass.It is widely reported that the eff for FN tunneling at low temperatures is found consistent with the B from TE at high temperatures 13.Hence,0.45 eV is adopted for eff in FN tunneling.Based on the FN slope of-12.4 MV/cm,m*is estimated to be 0.36 me,matching the commonly reported m*of 0.4 me in AlGaN 14.Combining Eq.4-6 and Eq.4-10 yields the MTTF at low temperatures and higher gate biases,as shown in Eq.4-11.After further expanding the current density term(Jn)as shown Eq.4-8 and 4-10,now a comprehensive gate lifetime model can be developed as shown in Eq.4-9 and Eq.4-11.When T 6 VGS,Max stays consistently at 70 ns for all parts.The testing switching frequency is approximately 3 MHz.Four different GaN HEMT products and three parts per product with a drain-source(VDS)rating from 50 V to 200 V were tested with a peak VGS of 7 V to a trillion pulses at 25C.Figure 7 shows the evolution of threshold voltage(VTH)and on-resistance(RDS(on)of a representative device from each product.Device characterization was conducted prior to testing and after reaching a trillion cycles.As shown in Figure 4,the post-stress measurements are well below the datasheet limit of each product.VDtTSTOFigure 4-6:Illustration of the 1Factor overvoltage specification,which is defined by the ratio between TO(overvoltage duration)and TS(switching period).Figure 4-7:(a)a simplified schematic of the inductive gate switching test system;(b)the measured inductor current and VGS waveforms with a peak voltage of 7 V.GateDriverVINVDDQDUTPulseL0.10.20.32024680.20.10.00.1Inductor Current(A)VGS of DUT(V)Time(s)Phase 1Phase 2VGSIL(a)(b)00.20.40.60.81Cycle(Trillion)11.522.5VTH(V)VTH,MAXVTH,MAXVTH,MAXRDS(on),MAXRDS(on),MAXRDS(on),MAXRDS(on),MAX77.588.5RDS(on)(m)Cycle(Trillion)VTH (V)RDS(on)(m)Cycle(Trillion)VTH (V)RDS(on)(m)Cycle(Trillion)VTH (V)RDS(on)(m)EPC2057(50 V)00.20.40.60.8111.522.588.599.51010.511EPC2252(100 V)00.20.40.60.8111.522.53.544.555.56EPC2308(150 V)00.20.40.60.8111.522.5678910EPC2307(200 V)Figure 4-8.Parametric comparison of pre-and post-stress,1-trillion gate overvoltage spikes with a peak voltage of 7 V of four representative GaN HEMT products,including EPC2057,EPC2252,EPC2308,and EPC2307.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|10Additionally,two representative products(EPC2057 and EPC2307)were subjected to another trillion pulses of stress with a peak VGS of 7 V,while the junction temperature was maintained at 125C.Figure 4-9 shows the static parameter measurements after an additional trillion cycles of stress at 125C,where no significant shift was observed.00.511.5211.522.56.577.588.500.511.5211.522.55678910Cycle(Trillion)VTH(V)VTH,MAXRDS(on),MAXRDS(on)(m)Cycle(Trillion)VTH(V)VTH,MAXRDS(on),MAXRDS(on)(m)EPC2057 EPC230725C25C125C125CFigure 4-9.On-resistance(RDS(on)and threshold voltage(VTH)parametric comparison of pre-and post-stress,2-trillion pulses with peak voltage of 7 V at 25C(blue shaded)and 125C forced heating(orange shaded)of EPC2057 and EPC2307.A total of 12 GaN HEMTs have been subjected to a total of 15 trillion pulses of stress with a peak gate voltage of 7 V at the time of writing this report.Since each device was tested with a consistent time interval of approximately 70 ns with VGS 6 V,the total stress time is calculated to be approximately 1.1 x 106 seconds,which is one third of the projected gate lifetime at 7 V static gate bias with 100-ppm failure rate(3.3 x 106 seconds).Since no observable parameter shift has been detected,this suggests that there is still a significant margin in lifetime before the GaN HEMTs show any measurable parametric degradation.More testing is underway to further validate the applicability of the proposed 1Factor specification.However,the inductive gate overvoltage switching test results to date support a repetitive transient gate overvoltage rating of 7 V with a 1Factor.To demonstrate how to implement the 1Factor overvoltage specification,an example is provided.If a converter operates at 1 MHz switching frequency(TS=1 s),a repetitive overvoltage spike occurs during the gate turn-on transients due to unoptimized gate loop inductance.The spike has a peak VGS of 7 V with a time interval of 8 ns above 6 VGS.Dividing 8 ns by the 1 s of TS yields 0.8%,which is below the 1Factor.Therefore,a failure rate much lower than 100 ppm is expected after 10 years of continuous operation.4.2.Drain Wear-Out 4.2.1.Introduction to Drain Wear-Out MechanismsDynamic on-resistance(RDS(on)is one of the most common reliability concerns for GaN HEMTs when subjected to high drain-source bias stress.Dynamic RDS(on)refers to the condition in which the on-resistance of the GaN HEMTs increases when the device is exposed to high drain-source voltage(VDS).In this section,a similar test-to-fail method is used to investigate drain-related wear-out mechanisms.After understanding the underlying mechanisms responsible for dynamic RDS(on),a comprehensive physics-based drain lifetime model was developed from first principles to project dynamic RDS(on)shifts with respect to various parameters,including voltage,temperature,frequency,and current.GaN HEMTs are increasingly deployed in advanced applications,featuring high switching frequencies and fast slew rates.Thus,reliability and robustness under repetitive transient drain overvoltage stress have become another frequently asked reliability question by users.Later in this section,a similar duty cycle-based repetitive drain overvoltage specification was developed by using a resistive hard-switching testing circuit,which was subsequently validated through the development of an inductive switching test circuit.4.2.2.Development of Physics-Based Lifetime Models for Dynamic RDS(on)As discussed in the previous reliability reports 1,the dominant mechanism responsible for the dynamic RDS(on)failure mode is electron trapping at or near high electric field regions,leading to the depletion of 2DEG electrons within the drift region.Figure 4-10 shows a magnified image of an EPC2016C GaN HEMT displaying thermal emissions in the 12 m optical range.These emissions observed in such wavelength range are consistent with hot electron mechanism.After aligning the emissions with the device layout,it was found that these hot electron emissions occur in areas where the highest electric fields are present under high drain-source bias.This critical finding has led to the development of the next generation GaN HEMTs in which the peak electric fields are carefully managed to minimize dynamic RDS(on).Therefore,the latest generation GaN HEMTs exhibit nearly no dynamic RDS(on).RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|11Figure 4-10:A magnified image of an EPC2016C GaN transistor showing light emission in the 12 m wavelength short-wave infrared light range(SWIR)that is consistent with hot electrons.The SWIR emission(red-orange)has been overlaid on a regular(visible wavelength)microscope image and a semi-transparent image of the design photomask(purple).Figure 4-11:Illustration of the self-limiting trapping process,where the barrier height is enhanced after the most energetic electrons are trapped.The dynamic barrier change is be quantified as QS,where is a geometric factor that correlates the dynamic barrier height increase with respect to the trapped charges(QS).After understanding the fundamental wearout mechanism responsible for dynamic RDS(on),a comprehensive lifetime model was developed to describe the rise in dynamic RDS(on)of GaN HEMTs.This model was also derived from first principles under hard-switching test conditions.The model is predicated on the assumption that hot electrons are injected over a surface potential into the conduction band of the dielectric layer(e.g.Si3N4),where the electric field is highest.Figure 4-11 illustrates the band structure at the interfaces of GaN layer/AlGaN barrier layer/Si3N4 dielectric layer.After the more energetic electrons overcome the barrier and become trapped in the dielectric layer,those trapped charges(QS)exert an additional electrostatic screening force against the electrons in the 2DEG,causing a dynamic barrier height increase.Further barrier height enhancement hinders other energetic 2DEG electrons from getting trapped,which leads to a self-limiting trapping process.Since these hot electrons are created during the hard-switching transitions,the transient combination of high injection current and high fields leads to a hot carrier energy distribution with long tails in the high energy regime.This self-limiting electron trapping rate can be modeled by the integral of the electron density distribution function(f(E)bounded by the energy barrier bi QS to infinity where virtually no electrons can overcome the energy barrier,and the trapping process ultimately stopped,as shown in Eq.4-13.Dielectric(e.g.Si3N4)AIGaNGaNQsbif(E)dEEq.4-13/Eq.4-14where the electron density distribution,f(E),is exponentially depen-dent on electron energy(E),as shown in Eq.4-14 16,18.where f is electric field,q is electron charge,and is electron mean free path.Therefore,the QS is solved and shown in Eq.4-15.Under typical operation conditions,where the applied VDS does not exceed 120%of the VDS,Max,the QS is expected to be significantly less than the built-in piezoelectric charges in the 2DEG,QP 2,3.Additionally,another assumption is that once the electrons are trapped,they are trapped permanently(no de-trapping).Therefore,the final expression to define the dynamic RDS(on)shift,RDS(on)/R0 is shown in Eq.4-16.where VDS is the drain-source voltage,T is device junction temperature in Kelvin unit,t is testing time in minutes.Other parameters in the mathematical model were fitted to the measured results across a range of drain voltages and temperatures,where a is a unitless fitting parameter,b equals 2.0E-5(K1/2),L0 is 92 meV,corresponding to the LO phonon energy level scattered by the hot electrons,VFD is 100 V for generation-5(Gen5)100 V products only,and equals 10 V.Therefore,dynamic RDS(on)shift can be modeled by a linear relation with respect to logarithmic of test time(log-t)under hard-switching conditions.Figure 4-12 shows the voltage and temperature dependence of dynamic RDS(on)for a fifth-generation EPC2045 GaN HEMT with a maximum drain-source voltage rating of 100 V(VDS,Max=100 V).The results showed that the measurement data points followed the logarithmic-time lifetime projection,validating the effectiveness of the lifetime model in Eq.4-16.Eq.4-15 Eq.4-1600exp log RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|12On the top graph of Figure 4-12,the EPC2045 devices were tested at 25C with the applied drain-source voltage ranging from 60 V to 120 V.The results show that the dynamic RDS(on)increases as a function of drain-source voltage(VDS).As the VDS increases,the peak electric field increases,which accelerates the hot electron trapping effect,leading to more significant dynamic RDS(on)rise over time.The graph on the bottom shows the time evolution of RDS(on)when biased at 120 V across three different temperatures:25C,75C and 125C.The counter-intuitive result shows that dynamic RDS(on)effect becomes more prominent at lower temperatures than at higher temperatures,which is consistent with hot-carrier injection theory.At lower temperatures,these energetic electrons can travel further between scattering events from the LO-phonon,gaining greater kinetic energies under a given electric field.When the hot electrons are accelerated to higher energies,they can reach deeper layers in which charge trapping becomes more likely.This finding also suggests that traditional testing methods,such as high temperature 1000 hours25000 hours10 years1000 hours25000 hours10 years Normalized RDS(on)101102103104105106Time(min)RDS(on)Shift vs.TemperatureRDS(on)Shift vs.Time and VIN(25C)125C25C75C21.81.61.41.210.80.60.40.20 Normalized RDS(on)101102103104105106Time(min)21.81.61.41.210.80.60.40.20120 V100 V80 V60 VEPC2045100 kHzEPC2045120 V,100 kHzFigure 4-12:The RDS(on)of a fifth generation EPC2045 GaN transistor over time at various voltage stress levels and temperatures.On the top,the devices were tested at 25C with voltages from 60 V to 120 V.The graph on the bottom shows the evolution of RDS(on)at 120 V at various temperatures.Eq.4-17=a1a1a2 log(1 a3t/)1 a2 log(1 a3t/)RRCQPa21QPa3 B where:a1=0.6(unitless)a2=b/a1(where b=2.0E-5 K1/2 from 19)a3=1000(K1/2 min1)b=2.0E-5(K1/2)L0=92 meVVFD=100 V(appropriate for Gen5 100 V products only)=10(V)T=Device temperature(K)t=Time(min)with the following expanded list of parameters:reverse bias(HTRB),where a device is tested at maximum drain-source voltage and temperature for a long duration,may not be enough to determine the reliability of a device.The model allows users to project long-term RDS(on)growth as a function of four key input variables:drain voltage,temperature,switching frequency,and switching current with the following observations.RDS(on)growth with time The slope of RDS(on)over time has a negative temperature coefficient(i.e.lower slope at higher temperature)Switching frequency does not affect the slope,but causes a small vertical offset Switching current does not affect the slope Negligible difference between inductive and resistive hard switching.4.2.3.Impact of Higher Drain-Source Voltage StressIn the case where the amount of trapped charge approaches the number of electrons available in the 2DEG(the surface trapped charges(QS)approaches the built-in 2DEG piezoelectric charge(QP),the simplifying assumption used to develop Equation 4-16 is no longer valid.This situation could occur when devices are taken to voltages well above their design limits.Figure 4-13 shows results for EPC2045 devices tested up to 150 V at 75C and 125C.Note how the straight-line extrapolation that would occur with a simple log(time)dependence is no longer applicable.By removing the simplified assumption that only a small fraction of QP is trapped and transform into QS,the result shown in Eq.4-17 is obtained.Calculating Eq.4-17 using the expanded list of parameters yields the solid fit lines in Figure 4-13,providing further evidence of the validity and applicability of this physics-based model.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|13Time(min)150 V120 V100 V150 V,75C150 V,125C80 V60 VNormalized RDS(on)of EPC2045 at TC=75C and 100 kHzProjected RDS(on)of EPC2045 at 150 V and 100 kHzNormalized RDS(on)Time(min)Normalized RDS(on)1011021021031041041051061062.01.81.61.41.21.00.80.60.40.202.01.81.61.41.21.00.80.60.40.20Figure 4-14:(Left)200 V EPC2215 normalized RDS(on)at three voltages.Note that 280 V is 40ove the maximum rated voltage.(Right)EPC2215 at 75C and 125C and 200 V.The solid lines are the model results using variables for 200 V devices,and the dots are actual measurements.Figure 4-15:Evolution of RDS(on)of a representative EPC2045 device,a fifth-generation 100 V rated GaN transistor,tested at 120 V and 75C.It is projected to exceed 20%RDS(on)shift at 2 x 105 minutes by considering 90%of upper bound confidence level.Figure 4-14 compares this model to measurements of 200 V devices.On the left is the normalized RDS(on)for the fifth-generation,200 V rated EPC2215 at three voltages.The highest voltage,280 V,is 40ove the maximum rating.On the right are measurements compared with the model at two different temperatures and the maximum rated voltage.4.2.4.Development of a Repetitive Transient Drain Overvoltage Specification Transient drain voltage overshoot is commonly observed in GaN-based converters due to high slew rate and fast switching applications.A survey of transient overvoltage specification from a suite of GaN suppliers was conducted by JEDEC JC-70 committee and presented in JEP186 20.Most of the transient overvoltage specifications describe it as a device robustness indicator.In addition,many of them consider drain voltage overshoot as a single rare event or atypical occurrence.Hence,it is challenging for application engineers to effectively implement these specifications into their designs.Therefore,an application driven,and user-friendly repetitive transient off-state drain overvoltage specification on datasheets is important for the general adoption of GaN technology because of the absence of avalanche mechanisms in GaN HEMTs.A resistive hard-switching test system 1,15 was employed to study dynamic RDS(on)shift under cumulative drain overshoot stress,where this system operates at 100 kHz,85%of the time reverse-biasing the GaN device under test(DUT)at the specified off-state drain voltage.When determining time of failure,20%of RDS(on)shift compared to the initial RDS(on)value after a projected 25 years of stress is used as the failure criteria.Eq.4-14 is used to extrapolate the time-of-failure when the in-situ monitored RDS(on)shifts more than 20%to its initial value(R0).This approach is more stringent than the typical datasheet maximum RDS(on)limit.A suite of 100 V fifth generation GaN products were tested by the resistive hard switching test circuit at 120%of VDS,Max and 75C junction temperature,a common mission temperature.EPC2045,the first generation-5 100 V drain-source rated GaN product,was subjected to testing under such accelerated hard-switching conditions.Figure 4-15 shows the testing results,where the DUT is projected to exceed the 20%RDS(on)shift limit at approximately 2 x 105 minutes by considering a 90%upper bound confidence level.Lifetime extrapolation is based upon the logarithmic time relation.It is noted that this is a more conservative estimated time of failure than the actual projected lifetime that is estimated to be nearly 1 x 106 minutes.By multiplying by 85%,it yields 1.7 x 105 minutes,representing the total lifetime when the DUT is off state biased continuously under 120 V and 75C.When comparing with 25 years of expected overall lifetime,equivalent of 1.3 x 107 minutes,it translates to approximately 1.3%of total lifespan in mission.To add more margin,we rounded to 1%of 25 years.Now a total lifetime-based overvoltage specification of 1.3 x 105 minutes is developed.Normalized RDS(on)101102103104105106Time(min)75C2.01.81.61.41.21.00.80.60.40.20EPC2045120 V,100 kHz2 x 105 minRELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|14To further validate this total time-based specification,the same testing conditions were applied to newer 100 V rated GaN products including EPC2218,EPC2071,EPC2302,and EPC2204.Figure 4-16 summarizes the testing results of the listed products,where they are all projected to outperform the 1.3 x 105 minutes of lifetime.A number of 100 V rated GaN transistors from different wafer lots are stressed by a 120 VDS,Peak overvoltage spike at 100 kHz operation frequency and 75C junction temperature.Figure 4-19 shows that representative EPC2218 devices from three different wafer lots were tested to over billions of switching cycles showing very small dynamic RDS(on)shift 18.The same physics-based lifetime model based on hot carrier trapping was applied to project the lifetime under such drain overvoltage stresses.The projection demonstrates the excellent robustness of GaN devices under 120%overvoltage stress over long-term continuous operation.At each switching cycle,the duration exceeding 100 VDS,Max is approximately 25 ns,lower than the 120 V peak overshoot voltage.At the end of 8 x 108 seconds(25 years),which equates to 8 x 1013 total pulses by multiplying with 100 kHz frequency,none of the DUTs surpassed the 20%RDS(on)shift failure criteria.Multiplying 25 ns by 8 x 1013 pulses gives 2 x 105 minutes,which is close to the estimated total lifetime of 1.3 x 105 minutes.The slight difference can be explained by the fact that the DUTs only reach the 120 V peak voltage for a very short portion of each pulse.The voltage waveform shown in Figure 4-18 is more representative of real time circuit applications.This total time-based specification can be scaled to a shorter duration that occurs repetitively within each switching cycle.Therefore,another way to specify this repetitive rating is to calculate the ratio of overvoltage duration of each cycle over the switching period,which is the 1%scaling factor that was initially discussed.This is equivalent to calculating the duty cycle of the overvoltage spike.For instance,if a converter operates at 100 kHz,equivalent of 10 s per switching period,it suggests that the GaN devices should withstand a repetitive 120 V overvoltage spike with a 100 ns duration in each switching cycle over 25 years of lifetime.This mathematical relation is demonstrated in Eq.4-18 and further illustrated in Figure 4-17.To verify this newly proposed overvoltage specification method,an unclamped inducive switching(UIS)circuit was developed 18.Figure 4-18 shows the resulting overvoltage pulse that is generated by UIS.where TO is the overvoltage duration within each switching period and TS is the switching period.1 x 106 min25 years Normalized RDS(on)100102104106108101103105107Time(min)2.01.81.61.41.21.00.80.60.40.20EPC2204EPC2218EPC2071EPC2302VDS=120 VFigure 4-16:Evolution of RDS(on)of representative EPC2204,EPC2218,EPC2071,and EPC2302 GaN transistors,rated at 100 V and tested at 120 V and 75C.They are projected to have less than 20%RDS(on)shift at a minimum of 1 x 106 minutes,significantly exceeding the 2 x 105 minutes lifetime based on EPC2045.Figure 4-18:Simplified schematic of the unclamped inductive switching circuit and the resulting overvoltage pulse with VDS,Peak of 120 V under 100 kHz operating frequency.Figure 4-17:Illustration of the 1%overshoot duty cycle overvoltage specification.1%is the ratio between TO(overvoltage duration)and TS(one switching period).Eq.4-18VDtTSTO140120100806040200-2000.050.10.150.2Drain Voltage(V)Time(s)VINCINVDVDS,PEAK=120 V at 100 kHz and 75CVSLDUTDriverClipper forIn-SituMonitoringOvershoot duty cycle=10%Overvoltage Duration at 75C(TO)Switching Period(TS)RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|15100102104106Time(minutes)Lot ALot BLot CRDS(on),Max of EPC2218(75C)=4.3 m 1.5 billion pulses RDS(on)(m)65432106 billion pulses10 billion pulses100102104106108Time(minutes)EPC2204EPC2302RDS(on),Max of EPC2218(75C)=4.3 m RDS(on),Max of EPC2302(25C)=1.8 m RDS(on)(m)876543210Figure 4-19:Evolution of dynamic RDS(on)of a representative EPC2218 DUTs from three different wafer lots under 120 VDS,Peak and 75C UIS testing for more than 1.5 billion cycles.Figure 4-20:Evolution of RDS(on)shift of a representative EPC2204 and EPC2302 DUTs under 120 VDS,Peak UIS testing.Two additional representative 100 V-rated GaN transistors,EPC2204 and EPC2302 were tested under 120 VDS,Peak by UIS at 25C shown in Figure 4-20.They were stressed for more than 6 and 10 billion pulses,respectively,where small dynamic RDS(on)drifts were measured.When projected to 25 years(1.3 x 107 minutes)and beyond,the dynamic RDS(on)of the DUTs are expected to be well below the maximum datasheet limit.The results further validated the proposed overvoltage specification.A repetitive drain overvoltage specification is proposed and validated by resistive load hard switching and unclamped inductive switching testing circuits.This duty cycle-based specification offers a more quantitative and easy-to-implement guideline for application engineers to design GaN devices.This work also demonstrates the extreme overvoltage robustness of GaN HEMTs.4.3.Current Density Wear-out4.3.1.Introduction to Current Density Wear-out MechanismsThermal limits can become a concern for GaN devices when high current and high drain-source voltage occur simultaneously.Extensive robustness testing was conducted,and the results validated of safe operating area specified in the datasheet.For certain applications,the capability to withstand short circuit fault conditions is a must.Therefore,short circuit testing was performed,where GaN demonstrated excellent robustness under such extreme stress conditions.When devices are exposed to continuous high current at elevated temperatures,electromigration(EM)robustness becomes a common concern for customers.Thus,accelerated EM testing was conducted on power quad-flat no-leads(PQFN)devices that utilize copper pillars as the interconnects between the device and the package.Based on the EM testing results,a continuous current rating was developed for PQFN products,which also demonstrates excellent EM robustness.Lastly,a pulsed current rating specification was developed for GaN at various gate drive voltages and temperatures.4.3.2.Safe Operating AreaSafe operating area(SOA)testing exposes the GaN transistor to simultaneous high current(ID)and high voltage(VDS)for a specified pulse duration.The primary purpose is to verify the transistor can be operated without failure at every point(ID,VDS)within the datasheet SOA graph.It is also used to probe the safety margins by testing to fail outside the safe zone.During SOA tests,the high-power dissipation within the die leads to a rapid rise in junction temperature and the formation of strong thermal gradients.For sufficiently high power or pulse duration,the device simply overheats and fails catastrophically.This is known as thermal overload failure.In Si MOSFETs,another failure mechanism known as secondary breakdown(or Spirito effect 21)has been observed in SOA testing.This failure mode,which occurs at high VD and low ID,is caused by unstable feedback between junction temperature and threshold VTH.As the junction temperature rises during a pulse,VTH drops,which can cause local current to rise.The rising current,in turn,causes temperature to rise faster,thereby completing a positive feedback loop that leads to thermal runaway and ultimate failure.The goal of this study is to determine if the Spirito effect exists in GaN transistors.For DC,or long-duration pulses,the SOA capability of the transistor is highly dependent on the heatsinking of the device.This can present a huge technical challenge to assess the true SOA capability,often requiring specialty water-cooled heatsinks.However,for short pulses(1 ms),the heatsinking does not impact SOA performance.This is because on short timescales the heat generated in the junction does not have sufficient time to diffuse to any external heatsink.Instead,all the electrical power is converted to raising the temperature(thermal capacitance)of the GaN film and nearby RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|161001010.11001010.1100 s pulse(pass)100 s pulse(fail)1 ms pulse(pass)1 ms pulse(fail)100 s pulse(pass)100 s pulse(fail)1 ms pulse(pass)1 ms pulse(fail)100 s pulse(pass)100 s pulse(fail)1 ms pulse(pass)1 ms pulse(fail)EPC2045 100 VEPC2212 100 V AEC0.1110VDS Drain-Source Voltage(V)1000.1110VDS Drain-Source Voltage(V)1001001010.1EPC2014C 40 V0.1110VDS Drain-Source Voltage(V)100Limited by RDS(on)Limited by RDS(on)ID Drain Current(A)ID Drain Current(A)ID Drain Current(A)Limited by RDS(on)silicon substrate.As a result of these considerations,SOA tests were conducted at two pulse durations:1 ms and 100 s.Figure 4-21 shows the SOA data of 200 V EPC2034C.In this plot,individual pulse tests are represented by points in(ID,VDS)space.These points are overlaid on the datasheet SOA graph.Data for both 100 s and 1 ms pulses data are shown together.Green dots correspond to 100 s pulses in which a part passed,whereas red dots indicate where a part failed.A broad area of the SOA was interrogated without any failures(all green dots),ranging from low VDS all the way to VDS,Max(200 V).All failures(red dots)occurred outside the SOA,indicated by the green line in the datasheet graph.The same applies to 1 ms pulse data(purple and red triangles);all failures occurred outside of the datasheet SOA.Figure 4-22 provides SOA data for three more parts,EPC2212(4th generation automotive 100 V),EPC2045(5th generation 100 V),and EPC2014C(4th generation 40 V).In all cases,the datasheet safe operating area has been interrogated without failures,and all failures occur outside of SOA limits,often well outside the limits.The datasheet SOA graph is generated with finite element analysis,using a thermal model of the device including all relevant layers along with their heat conductivity and heat capacity.Based on transient simulations,the SOA limits are determined by a simple criterion:for a given pulse duration,the power dissipation must be such that the junction temperature does not exceed 150C before the end of the pulse.This criterion results in limits based on constant power,denoted by the 45 green(100 s)and purple(1 ms)lines in the SOA graph.This approach leads to a datasheet graph that defines a conservative safe operating zone,as evidenced by the extensive test data in this study.In power MOSFETs,the same constant power approach leads to an overestimate of capability in the high voltage regime,where failure occurs prematurely due to thermal instability(Spirito effect).While the exact physics of failure is yet to be determined,the main outcome of this study is clear GaN transistors will not fail when operated within their datasheet SOA.1001010.10.11Limited by RDS(on)100 s pulse(pass)100 s pulse(fail)1 ms pulse(pass)1 ms pulse(fail)10VDS Drain-Source Voltage(V)EPC2034C 200 V100ID Drain Current(A)Figure 4-21:EPC2034C SOA plot.The“Limited by RDS(on)”line is based on datasheet maximum specification for RDS(on)at 150C.Measurements for 1 ms(purple triangles)and 100 s(green dots)pulses are shown together.Failures are denoted by red triangles(1 ms)or red dot(100 s).Note that all failures occur outside the datasheet SOA region.Figure 4-22:SOA results for EPC2014C,EPC2045,and EPC2212.Measurements for 1 ms(purple triangles)and 100 s(green dots)pulses are shown together.Failures are denoted by red triangles.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|174.3.3.Short-Circuit Robustness Short circuit robustness refers to the ability of a FET to withstand unintentional fault conditions that may occur in an application while in the ON(conducting)state.In such an event,the device will experience the full bus voltage combined with a current that is limited only by the inherent saturation current of the transistor and the circuit parasitic resistance,which varies with the application and location of the fault.If the short-circuit state is not quenched by protection circuitry,the extreme power dissipation will ultimately lead to thermal failure of the transistor.The goal of short-circuit testing is to quantify the“withstand time”the part can survive under these conditions.Typical protection circuits(e.g.,de-saturation protection for IGBT gate drivers)can detect and react to over-current conditions in 23 s.It is therefore desirable if the GaN transistor can withstand unclamped short-circuit conditions for about 5 s or longer.The two main test circuits used for short-circuit robustness evaluation are described in 22.They are:Hard-switched fault(HSF):gate is switched ON(and OFF)with drain voltage applied.Fault under load(FUL):drain voltage is switched ON while gate is ON.For this study,devices were tested in both fault modes and no significant differences in the withstand time were found.Therefore,the focus will be on FUL results for the remainder of this discussion.However,it is important to note that from HSF testing,GaN transistors did not exhibit any latching or loss of gate control that can occur in silicon based IGBTs 23.This result was expected given the lack of parasitic bipolar structures with the GaN devices.Until the time the transistors fail catastrophically,the short circuit can be fully quenched by switching the gate LOW,an advantageous feature for protection circuitry design.Two representative GaN transistors were tested:1.EPC2203(80 V):4th generation automotive grade(AEC)device2.EPC2051(100 V):5th generation deviceThese devices were chosen because they are the smallest in their product families.This simplified the testing owing to the high currents required for short-circuit evaluation.However,based on simple thermal scaling arguments,the withstand time is expected to be identical for other in-family devices.EPC2203 results cover EPC2202,EPC2206,EPC2201 and EPC2212;EPC2051 covers EPC2045 and EPC2053.Figure 4-23 shows fault-under-load data on EPC2203 for a series of increasing drain voltages.With VGS at 6 V(the datasheet maximum),and a 10 s drain pulse,the device did not fail all the way up to VDS of 60 V.Under these conditions,over 1.5 kW is dissipated in a 0.9 x 0.9 mm die.At the higher VDS,the current is seen to decay over time during the pulse.This is a result of rising junction temperature within the device and does not signify any permanent degradation.70605040302010050403020100-104030201000510152025Drain-Source Voltage PulsesDrain Current PulsesOutput I-V of EPC2203Drain-Source Voltage(V)Drain Current(A)Drain Current(A)Time(s)0510152025Time(s)10-1100101102Drain-Source Voltage(V)VGS=6 VVGS=6 VVGS=6 VFigure 4-23:EPC2203 fault under load test(FUL)waveforms for a series of increasing drain voltages.Drain pulse is 10 s and VGS=6 V.The device did not fail for this pulse width.In the VDS vs.time plot(left),VDS is Kelvin-sensed directly at the device terminals.In the IDS vs.time plot(center),it is noted that IDS decreases over time due to self-heating.Resulting output curve for this test sequence(right).Drain current is reported as the average current during the pulse.Drain current rolls over in the saturation region owing to device heating at higher VDS.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|18Using a longer pulse duration(25 s),the parts eventually fail from thermal overload.Representative waveforms are shown in Figure 4-24.The time of failure is marked by the abrupt sharp rise in drain current.After this event,the devices are permanently damaged.The withstand time is measured from the beginning of the pulse to the time of failure.To gather statistics on the withstand time,cohorts of eight parts were tested to failure using this approach.Table 4-1 summarizes the results.EPC2203 was tested at both 5 V(recommended gate drive)and 6 V(VGS(max),with mean withstand time of 20 s and 13 s,respectively.Note that the device survives less time at 6 V because of the higher saturation current.EPC2051 exhibited a slightly lower time-to-fail(9.3 s)compared with the EPC2203 at 6 V.This is expected because of the more aggressive scaling and current density of 5th generation products.However,in all cases,the withstand time 100806040200200150100500100806040200100806040200051015202530Unclamped Short-Circuit EPC2203Unclamped Short-Circuit EPC2051Drain-Source Voltage(V)Drain Current(A)Drain Current(A)Drain-Source Voltage(V)Time(s)051015202530Time(s)Figure 4-24:Fault-under-load test waveforms for a typical EPC2203(left)and EPC2051(right)at VDS=60 V,VGS=6 V,and a 25 s drain pulse.The abrupt rise in drain current marks the time of catastrophic thermal failure.Figure 4-25:Simulated junction temperature rise versus time during the short-circuit pulses for both EPC2051 and EPC2203 at both 5 V and 6 V VGS.Measured failure times are indicated by red markers.Note that EPC2203 fails catastrophically at a TJ of around 475C,whereas EPC2051 fails around 575C.The simulated TJ is well fit by a simple square root dependence on time(heat diffusion),as shown in the equation.P denotes the average power per unit area,and k=6.73 x 105 Km2/Ws1/2.is comfortably long enough for most short-circuit protection circuits to respond and prevent device failure.Furthermore,the withstand time showed small part-to-part variability.The lower rows in Table 4-1 provide pulse power and energy relative to die size.To gain insight into the relationship between these quantities and the time to failure,time-dependent heat transfer was simulated to determine the rise in junction temperature TJ during the short-circuit pulse.The results are shown in Figure 4-25.Note:Statistics derived from eight devices in each condition.Withstand times are tightly distributed around mean value.Average pulse power and energy correspond to a typical part within the population.Table 4-1:Short-circuit withstand time statistics for EPC2203 and EPC2051 Short-circuit pulseVDS=60 VEPC2203(Gen 4)EPC2051(Gen 5)VGS=6 VVGS=5 VVGS=6 VVGS=5 VMean TTF(s)13.120.09.3321.87Std.dev.(s)0.780.370.212.95Min.TTF(s)12.119.69.0818.53Avg pulse power(kW)1.7641.43.032.03Energy(mJ)23.8327.627.7142.49Die area(mm2)0.90251.105Avg power/area(kW/mm2)1.951.552.741.84Energy/area(mJ/mm2)26.430.5925.0838.4660050040030020010000510152025TJ=k P t1/2EPC2051(6 V)Short-circuit Time(s)Junction Temperature Rise(C)EPC2203(6 V)EPC2051(5 V)EPC2203(5 V)RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|19The intense power density during the pulse leads to rapid heating in the GaN layer and nearby silicon substrate.Because the pulse is short and heat transfer is relatively slow,only a small thickness of semiconductor(100 m in depth)can help to absorb the energy.The temperature grows as the square root of time(characteristic of heat diffusion),and linearly with the pulse power.As can be seen in Figure 4-25,for EPC2203,both the 5 V and 6 V conditions fail at the same junction temperature rise of 475C.The same is true for EPC2051,where both conditions fail at the same TJ of 575C.Three key conclusions stem from these results:1.For a given device,the time to failure is inversely proportional to the power dissipation squared(P-2).This applies for short-circuit and SOA pulses of duration 400C)that are totally inaccessible to silicon devices owing to free-carrier thermal runaway.4.3.4.Development of a Continuous Current Rating for PQFN GaN HEMTs Copper pillars are used as the interconnects in the latest EPCs GaN HEMTs that utilize power quad flat no-leads(PQFN)packages.The copper pillar interconnects consist of two parts:a plated copper pillar and a solder cap that is mainly composed of Tin(Sn)with varying trace amounts of Silver(Ag),Gold(Au),and Copper(Cu)24,25,26,27,28.After the reflow process,the solder cap connects the die and the package and is typically considered as the limiting factor for the continuous current rating of GaN HEMTs.Electromigration(EM)has been identified as the primary wear-out mechanism,defined as the movement of atoms in a metal structure,leading to void formation 29,30.Therefore,in this section,EM testing was conducted to determine the continuous current density limit for the copper pillars implemented in EPCs GaN HEMTs.Based on the test results,a continuous current rating is recommended with quantitative reliability implications.The primary cause of EM is the electron“wind”generated from the transfer of momentum between conducting electrons and metal ions in the crystal.When the momentum surpasses the diffusion threshold that is governed by an activation energy 30,31 metal atoms can move and create voids.The Blacks model is widely accepted to predict lifetime under EM wear-out mechanism,as shown in Eq.4-19 29,30.Where A is a constant,j is current density that is defined as current divided by the cross-sectional area of the copper pillar,n is an exponent,Q is the activation energy,k is the Boltzmanns constant at 8.62 x 105 eV/K,and T is the temperature in Kelvin unit.Eq.4-19 The jn term in Blacks equation models the solder wear-out,which is shown as void growth,is highly accelerated by current density.The initial formation of solder voiding,caused by EM degradation,reduces the cross-sectional area through which the current can flow,resulting in a further increase in current density.The increase in current density in turn further accelerates the solder void formation,which leads to a positive feedback loop.The eQ/kT term in Eq.4-19 represents the thermal activation process of EM.Joule heating raises the junction temperature,which accelerates the movement of atoms resulting in more void formation.Both processes can lead to an open circuit due to void formation or electrical shorts caused by the melting of the metal interconnect.Since EM is a slow mechanism that can take years to develop under normal use conditions,testing under accelerated stress conditions is necessary to generate EM related failures within a reasonable timeframe.The EM experiment consists of three parts,which include a device under test(DUT)card,a custom test chip,and a temperature chamber.The custom test chip was designed by following JEDEC standard,JEP154 32.The test setup is placed in a temperature chamber with the DUT card placed in the center.Two thermocouples were used:one mounted at the center of the oven to monitor the ambient temperature,and the other one is placed directly on the backside of the DUT,where the Si substrate is exposed.The test chip is covered with thermal putty and sandwiched between two copper heat sinks to maintain a constant temperature.The temperature difference between the copper pillar interconnect and the backside of the device,where the second thermocouple is placed,is calculated to be 0.64C by using the Rth,JC of 0.2C/W and a total of 3.2 Watts of power dissipated at 125C.The copper pillar interconnect of interest has an elliptical shape with an area of 5,271 um2 and is soldered onto a copper lead frame that is molded into a PQFN package outline.Test conditions of 27 kA/cm2 at 125C and 55 kA/cm2 at 150C were selected,based on previous research studies focusing on copper pillar interconnects 26,27,28,30.A failure criterion of 10%resistance increase was adopted according to the recommendations in JEP154 32.Both test conditions yielded zero failures,which is consistent with various studies that focus on EM copper interconnects 24,25,26,27,28.A current density power exponent of 2 has been frequently reported for copper pillar interconnects by various studies24,27.An activation energy of 1 eV is commonly accepted for SnAg solder cap through previous works 24,25,26,27,28.By using the values of n=2 and Q=1 eV and assuming the time to failure of 870 hours with 0.1ilure rate,the constant A of the Blacks equation is calculated to be 3.24.After determining the constant A,the lifetime at a 0.1ilure rate for any given temperature and current density can be calculated.The continuous current ratings of EPCs PQFN devices 24,30 are based on a conservative EM current density limit of 13 kA/cm2.By plugging in a current density of 13 kA/cm2 and a junction temperature of 125C into Eq.4-12,10 years of lifetime with 0.1ilure rate is projected.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|204.3.5.Development of a Pulsed Current Rating for GaN HEMTs In this section,a testing circuit was developed to systematically characterize the pulsed current rating of GaN HEMTs at various gate drive voltages and temperatures.After measuring a suite of GaN HEMTs,including various generation-5 and a representative generation-6 100 V drain-source rated devices,statistical analysis was performed to develop a recommendation for specifying the pulsed current rating of GaN HEMTs.Figure 4-26 illustrates the schematics of the testing circuit.First,the low-side GaN device under test(DUT)is biased at the specified DC gate voltage(VGS),while the high-side Si MOSFET(SIR500DP-T1-RE3)is turned on with a 25 s gate pulse signal with an input voltage(VIN)varying from 1 V to 5 V in steps of 0.5 V.During each pulse testing under a combination of VGS and VIN,measurements were taken using an oscilloscope to record the Kelvin-sensed drain-source voltage drop(VDS)across the DUT,and the voltage drop across the 1 m shunt resistor to calculate the drain-source current(IDS).OscilloscopeDriverC1VDVINVSiDShuntDUTVGSFigure 4-26:An illustration of the testing circuit to characterize the pulsed current rating under various VGS,VDS,and tem-peratures.First,a matrix of generation-5 GaN HEMTs,including commercial-grade(EPC2051 and EPC2070)and automotive-grade(EPC2252 and EPC2204A),were tested under 5 V and 5.5 V gate drive voltages,as well as 25C and 125C device junction temperatures.The 125C device junction temperature measurements were achieved by implementing a proportionalintegralderivative(PID)temperature controller directly mounted on the backside of the GaN devices,which have a low case-to-junction thermal resistance(RJC).Additionally,one generation-6 100 V drain-source voltage rated GaN HEMT(EPC2090)was tested under similar conditions.Figure 4-27 shows a representative cal-culated current waveform under 5 VGS and 3 VDS at 25C for EPC2252.The ex-tracted pulsed current under such test conditions is obtained by averaging the measured current from 15 s to 25 s with a pulse width of 10 s,as marked in Figure 4-27.Figure 4-28 summarizes all As shown in Figure 4-28,a drain-source voltage of approximately 3 V is identified as the typical inflection point for 80 V or 100 V rated parts,at which the current conduction of GaN HEMTs transitions from linear region to saturation region.Therefore,3 VDS Kelvin-sensed measurement is used to quantify the pulsed current density at various VGS and temperatures,with the results summarized in Table 4-2.Figure 4-27:a representative drain-source current waveform of EPC2252 under 5 VGS and 3 VDS at 25C,where a pulse width of 10 s is used for pulsed current extraction.Figure 4-28:(a)pulsed current density scaled by gate width(Wg)vs.VDS with a fixed VGS of 5 V at 25C;(b)pulsed current density scaled by gate width(Wg)vs.VDS with a fixed VGS of 5.5 V at 25C;(c)comparison of the pulsed current density of a representative EPC2070 device under 25C and 125C with a VGS of 5 V;(d)comparison of the pulsed current density of a representative EPC2070 device under 25C and 125C with a VGS of 5.5 V.51015202530Time(s)10 s0306090120150IDS(A)Single 25 s pulse EPC2252 VGS=5 V,T=25C00.10.20.30.412345Tc_C251250.10.150.20.250.30.350.412345Tc_C2512500.10.20.30.41234500.10.20.30.4ID/wg(A/mm)ID/wg(A/mm)ID/wg(A/mm)ID/wg(A/mm)12345VDS(V)VDS(V)VDS(V)VDS(V)Gen 6EPC2051EPC2070EPC2204AEPC2252Gen 6EPC2051EPC2070EPC2204AEPC2252the averaged pulsed current measurements within a pulse width of 10 s under various VDS,VGS and temperatures,where the vertical axis repre-sents the measured current scaled by the corresponding gate width(Wg)for comparative analysis.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|21Table 4-2 shows the mean pulsed current measurements with standard deviation,measured with a pulse width of 10 s under various test conditions.Device Junction Temperature(C)ID/Wg(A/mm)underVGS=5 V and VDS=3 VID/Wg(A/mm)underVGS=5.5 V and VDS=3 V25C0.27 /-0.02(A/mm)0.31 /-0.03 (A/mm)125C0.22 /-0.03(A/mm)0.25 /-0.04 (A/mm)The three main conclusions of the pulsed current experiment are summarized as follows:At a 5 V gate drive and 25C junction temperature,GaN HEMTs can consistently output a current density of more than 0.2 A/mm,even after considering three standard deviations.An approximately 15%increase in current output is expected when overdriving the gate from 5 V to 5.5 V.When the device junction temperature is increased from 25C to 125C,GaN HEMTs are expected to output approximately 20%less current due to the increased RDS(on).In addition to the single pulsed current testing summarized in Table 4-2,long-term robustness testing was performed on four EPC2306 GaN HEMTs with a pulsed current density of 0.33 A/mm at 25C,which is equal to the mean current density of 0.27 A/mm plus three standard deviations.The resulting drain-source current is more than twice the maximum pulsed current rating of 197 A,specified in the datasheet.Figure 4-29 shows that after 100 million pulses with a testing frequency of 5 Hz,the RDS(on)of all DUTs remain well below the datasheet maximum specifications,suggesting the robustness of the GaN HEMTs under such extreme pulsed current stress conditions.4.4.Thermomechanical Wear-Out4.4.1.Introduction to Thermomechanical Wear-Out Mechanisms The primary wear-out mechanism responsible for thermomechani-cal stress is solder joint cracking,which occurs due to a mismatch in the coefficients of thermal expansion(CTE)between the DUT,the solder interconnects,and the PCB.Thermomechanical stress 020406080100Number of Pulses(Million)2.42.62.83.0RDS(on)(m)RDS(on)(m)Figure 4-29:RDS(on)measurements before and after 100 million pulsed current stress with more than twice the maximum pulsed current rating of 197 A,specified in the datasheet.Figure 4-30 Illustration of stress on solder joints during temperature cycling.has emerged as a common concern in applications that experience frequent and large temperature swings.A comprehensive temperature cycling(TC)lifetime model is developed in this section,which includes device dimensions,bump size,TC test conditions,ramp rate,and various PCB properties.When the expected lifetime of chip scale packaged(CSP)devices is less than the customers specifications,underfill with the right materials properties is recommended to improve TC lifetime.Lastly,the TC thermomechanical lifetime model is applied to power cycling(PC)stress for PQFN packaged devices,where the thermomechanical stress arises from the non-uniform temperature gradient between the PQFN devices and the PCB,resulting from the repetitive on-and-off operations of the devices.4.4.2.Development of a Comprehensive TC Lifetime Model In previous reliability reports 1,15,the main wear-out mechanism mode under temperature cycling(TC)stress was identified as solder joint cracking 1.Coefficient of thermal expansion(CTE)mismatch between the materials namely the device,solder and PCB is attributed as the fundamental cause of this wear-out mechanism.The CTE values of a typical FR4 PCB 33,a wafer level chip scale package(WLCSP)GaN-on-Si device 34,and SAC305 solder 35,are provided in Table 4-3.Figure 4-26 illustrates the resulting stress caused by CTE mismatch during temperature cycling testing.Figure 4-30(a)shows the solder joint between the device and PCB in a neutral thermal stress position.As the temperature is lowered as in Figure 4-30(b),the PCB with the higher CTE value contracts more than the GaN device,creating strain on the solder joints.Similarly,when the temperature increases in Figure 4-30(c),the PCB undergoes more expansion than the device,again creating strain on the solder joints.c.b.a.MaterialCTE(ppm/C)Device4Solder23PCB(FR4)18Table 4-3:Common material coefficients of thermal expansionIn the following sections,a comprehensive TC lifetime model is developed by incorporating the effect of die size and bump shape,TC environmental test conditions and various PCB properties.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|224.4.2.1.Modeling the Effect of Die Size and Bump Dimension TC lifetime with respect to die size is typically modeled using the classic Coffin-Manson relation,where the devices under test(DUTs)are usually symmetrical in both the x and y directions 36.Additionally,most of the solder joints presented in those studies are ball grid array(BGA),where all the bumps have an identical shape.Thus,distance-to-neutral point-based TC lifetime models are frequently adopted and have proven to be effective 37.However,there is a lack of TC lifetime models that account for both asymmetrical die size and varying solder bump shapes with land grid array(LGA)solder bumps 38.In this section,a suite of wafer level chip scale package(WLCSP)GaN devices with varying die size and bump shapes were evaluated for temperature cycling performance under a consistent assembly and TC testing condition.The Weibull distribution plots are shown in Figure 4-31,which includes EPC2206,EPC2071,EPC2069,EPC2218,EPC2204,EPC2152,and EPC2215.The temperature cycling experiment was constructed to ensure that the only variables are the device dimensions and bump shape.These devices were mounted on identical test PCB boards using identical solder(SAC305).The standoff height(i.e.the solder height after assembly)of 130 m was maintained during the assembly process.This was verified by performing physical cross-section of the assembled boards.The temperature cycle range was from-40C to 125C,with a ramp rate of 15C/min and soak time of 10 minutes at the end points following industry standard JESD22-A104F 39.After every temperature cycling interval,an electrical screening was performed to determine the number of failures,where exceeding datasheet limits was used as the failure criteria.A test-to-fail approach was adopted,where the devices are tested until a 50ilure rate is achieved.The failure distribution was analyzed using a two-parameter Weibull distribution for each device using maximum likelihood estimation(MLE)40.The resulting Weibull fits are indicated by solid lines in the graph of Figure 4-31,and the Weibull characteristics are in Table 4-4.The corner solder joint cracking 102103104Cycles to Failure0.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.99 Probability of FailureWeibull Plots-40C to 125CEPC2152EPC2215EPC2071EPC2218EPC2069EPC2204EPC2206Figure 4-31:Weibull distribution fits to the experimental TC data of various CSP GaN productsFigure 4-32:Example of gate length and DNPmax for EPC2069 and EPC2071.was found to be the main wear-out mode throughout all devices analyzed by physical cross-sectioning and SEM inspection,establishing that wear-out of the smallest corner solder bump is the limiting factor for TC lifetime.The Mean-Time-To-Fail(MTTF)data from the Weibull distribution,measured in number of cycles,were compared to die area to check for die size correlation with TC lifetime,as shown in Eq.4-20.where A is a constant,Die Area is the area of die by multiplying the length with the width and n is the exponent.The resultant fit is judged by a goodness-of-fit(R2).A R2 value of less than 0.7 indicates a poor fit,suggesting that die area alone is unable to provide a good correlation with TC lifetime by following the commonly accepted lifetime models in literatures 40,41,42.The concept of“Maximum Distance from Neutral Point(DNPmax)”is introduced as shown in Figure 4-32.During TC stress,the center point of the device experiences the least stress compared to extremities of the device.This center point is defined as the neutral point,the distance from the neutral point to the farthest extremity of solder bump is defined as DNPmax.DeviceWeibull Shape ParameterCharacteristic Weibull Life(cycles)Mean Time to Fail(cycles)EPC22065.6797737EPC21525.610851003EPC22155.611991108EPC20715.614161309EPC22185.617641630EPC20695.618801737EPC22045.623892208Table 4-4:Weibull statistics for tested devicesEq.4-20EPC2069 10.56 mm2EPC2071 10.24 mm2L=0.72 mmL=1.195 mmDNPmax=2.5 mmDNPmax=2.5 mmDNPmax=2.5 mmDNPmax=2.3 mmDNPmax=2.3 mmDNPmax=2.3 mm3.25 x 3.25 mm4.45 x 2.3 mmBy combining Norris-Landzberg modified Coffin-Manson TC lifetime model 44 and the concept of DNPmax,the MTTF can be modeled by Eq.4-21,as reported by multiple researchers 45.The best fit to Eq.4-21 yielded an R2 value of 0.79,slightly improved compared with simply using the device area.However,it is still not considered a very good fit.Eq.4-21RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|23Failure analysis established the gate solder joint cracking at the device corner as the limiting factor for TC performance.A longer gate bump likely indicates a longer time to failure under TC stress and vice versa.Figure 4-31 and 4-32 show that different device sizes also have varying length of the gate solder bump.Therefore,the corner gate bump shape should also be considered along with the DNPmax for a more accurate TC lifetime model development.Because the gate bump width is similar for all devices studied,the bump length,denoted as L,is the primary parameter that is included in the following discussions.Thus,the length of solder bump L is factored into DNPmax,and effective DNP(DNPeff)is defined in Eq.4-22.The resulting fit is shown in Figure 4-33 and results in an R2 value of 0.99 using gate length factor a=-0.65,and power exponent n=1.4.Eq.4-22eff1.01.52.02.53.08001200160020002400Mean Time to Fail(MTTF)(cycles)DNPef(mm)EPC2204EPC2218EPC2069y=3160*x-1.4R2=0.99EPC2071EPC2215EPC2152EPC2206Figure 4-33:Measured MTTF under TC conditions of-40C to 125C vs.the effective DNP(DNPeff)of 7 different devices with varying die dimensions and bump shape,where the red dash line,based on Eq.4-16,provides an excellent fit to the measured MTTF.The fitted power exponent of 1.4 shown in Figure 4-33 is consistent with other literature results 46,47,where exponents between 1 and 2 are frequently reported in SAC305 solder joint cracking failures under TC stress with similar test conditions.In summary,a TC lifetime model is proposed considering the device size and corner gate bump shape,This study establishes a temperature cycling lifetime model based on solder joint cracking caused by CTE mismatch from materials which takes into consideration the varying dimensions of both die and solder joints.COMSOL finite element analysis(FEA)simulations were carried out to validate the TC lifetime model presented in Eq.4-23.Anand viscoplasticity model for the SAC305 solder was implemented in Eq.4-230.65effCOMSOL,simulating solders plasticity and creep behavior during temperature cycling 47,48.Hence,the energy dissipation density of the solder bumps can be calculated based on the area of stress-strain hysteresis loops,denoted as W.Deverauxs energy-based fatigue model was subsequently used to calculate the MTTF,quantifying when the solder joint cracking initiates and eventually propagates through the entirety of the gate bump length,L,shown in Eq.4-24 48,49.Table 4-5 shows that the simulated MTTF is within /-10%error margin compared to experimental MTTF.This further validates the effectiveness of the proposed TC lifetime model in Eq.4-17 that includes device dimensions and the critical corner gate bump length(L).where the first term,K1WK2 represents the crack initiation lifetime and the second term,models the crack growth lifetime.K1,K2,K3,and K4 are fitting coefficients.Eq.4-24MTTFProduct AreaExperimentalMTTF(cycles)COMSOLSimulated MTTF(cycles)MTTFEPC261922082284 3%EPC221816301537-6%EPC206917371849 6%EPC2206737792 7%Table 4-5:A summary of the modeled MTTF using COMSOL FEA vs.the experimental measured MTTF column,showing that the simulation agrees with the experimental data within 10%.4.4.2.2.Modeling the Effect of TC Test Conditions In this section,a comprehensive TC lifetime equation is developed to model various TC test conditions,including the temperature difference between the hot and cold temperature extremes(T),the hot temperature extreme(TMax),the ramp rate(R)and the dwell time at temperature extremes(tDwell).First,TC experiments with different T were performed on EPC2218A WLCSP devices,with both test legs using similar ramp rate(R)and dwell times(tDwell)at the temperature extremes.After every temperature cycling interval,electrical screening was performed,in which exceeding datasheet limits was used as the failure criteria.The two test conditions are TC1:40C to 125C with T=165C and TMax=125C,and TC2:40C to 105C with T=145C and TMax=105C.Figure 4-34 shows the Weibull distribution analysis of the two TC experiments,where TC1 with a larger T and TMax accelerated TC failures more than TC2.Therefore,the Norris-Landzberg lifetime model was used and shown in Eq.4-25 44.Eq.4-25 expNTC=ARELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|24where NTC is the number of TC cycles to fail,f is the cycling frequency,describing the total number of cycles per day,and is the cycling frequency exponent,which is typically specified as 1/3 50,51,52,53,54.T defines the difference between TMax and TMin within one cycle and is the temperature range exponent,typically dependent upon the solder type and properties.Since SAC305 solder is used in this study,a value of 2 for is used based on literature 50,51,52,55,56.The last variable is the exponential term in Eq.4-25,which is an Arrhenius term focusing on the creep mechanism at the maximum temperature,TMax.Ea is the activation energy,k is the Boltzmann constant,8.62 x 105 eV/K.The activation energy(Ea)at TMax was calculated to be 0.2 eV,based on Table 4-6.This study forms the basis for the temperature-cycling reliability analysis of solar and DC-DC converters presented in Sections 5.1.6 and 5.2.5,respectively.In the Norris-Landzberg model,the frequency term(f)combines both the ramp rate and dwell time into a single term with the same exponent,which assumes that these two components have the same behavior and weight in relation to the MTTF.However,in many cases,the experimental results contradict the models projections 400600100020004000Cycles to Failure0.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.99 Probability of FailureTC1-40C to 125CTC2-40C to 105C20040080012001600Cycles to Failure0.01 0.02 0.05 0.10 0.25 0.50 0.75 0.90 0.95 0.99 Probability of Failure14C/min4C/minFigure 4-34.Weibull plots of temperature cycling results for EPC2218A under TC1 and TC2 test conditions,where devices are mounted on 2-copper-layer PCBs.Figure 4-35.Weibull plots of EPC2206 with two different ramp rates in the temperature profile.The slow ramp rate=4C/min and the fast ramp rate=14C/min.TC ConditionTMin(C)TMax(C)Characteristic Weibull LifeMTTF(cycles)TC1without underfill16540361505TC2without underfill14530482430TC1with underfill16540367230(Lower bound confidence level)Table 4-6:Temperature cycling profile and MTTF determined by Weibull plots57.Therefore,a further set of TC experiments was conducted to deconvolute the frequency term in Eq.4-25 into separate ramp rate term(R)and dwell time term(tDwell),each with its own power exponent.EPC2206 was used as the DUTs.In this new study,the ramp rate(R)was varied from an average of 4C/min to 14C/min using a single-zone environmental TC chamber,while all other TC testing parameters remained consistent.Figure 4-35 shows the Weibull plots of the two TC experiments with different ramp rates.Eq.4-26 is proposed to further define the ramp rate(R)and dwell time(tDwell),based on Eq.4-25.Figure 4-35 shows that the MTTF of the fast TC chamber(R=14C/min)is 829 cycles,which 13%less than that of the slow TC chamber(R=4C/min),with a MTTF of 952 cycles.Therefore,the ramp rate exponent,a,is estimated to be-0.134.The dwell time exponent,b,is-1/3 based on literature 50,51,52,53,54,suggesting that longer dwell time at TC temperature extremes lead to lower TC lifetime.Therefore,Eq.4-26 can be simplified to Eq.4-27 in terms of the TC ramp rate(R).Figure 4-36 shows the normalized TC lifetime as a function of the TC ramp rate under-40C to 125C test conditions,with all TC lifetimes normalized to that of the 15C/min ramp rate.15C/min is used because it is the most commonly referenced TC ramp rate in the JEDEC standard 39 for evaluating the reliability of solder interconnects.Therefore,users can refer to Figure 4-36 to extrapolate the TC lifetime at different TC ramp rates based on the existing TC data.Eq.4-26exp Eq.4-27.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|2524681012141618200.951.001.051.101.151.201.25Normalized TC LifetimeRamp Rate(C/min)Figure 4-36:Normalized TC lifetime vs.ramp rate on a 2-layer PCB under TC condition of-40C to 125C,with all TC lifetimes normalized to 15C/min ramp rate.Figure 4-38:Illustration of the in-plane tensile shear forces acting on the device and PCB.Figure 4-37:Stress-strain hysteresis loop of the slow and fast ramp rate groups.Finite Element Analysis(FEA)simulations were performed using COMSOL Multiphysics to investigate the underlying mechanism responsible for TC ramp rate effect on solder joint lifetime.The stress-strain hysteresis loops for both ramp rate groups are illustrated in Figure 4-37.The higher ramp rate group exhibits higher stress levels compared to the slow group,resulting in increased energy dissipation density and,consequently,a shorter TC lifetime.The higher strain rate under higher ramp rate can lead to a more significant strain hardening effect in the SAC305 solder,thereby generating elevated stress levels within the solder 48.Therefore,the simulation results predict that the MTTF at R=14C/min is 10.1%lower than that at R=4C/min,which agrees reasonably well with the experimental difference of 13%.4.4.2.3.Modeling the Effect of PCB Properties High-density power modules often utilize high-layer count and thick printed circuit boards(PCBs).Such implementations raise concerns about solder joint reliability during TC due to the increased stiffness of these complex PCBs.The influence of PCB properties on solder joint-15-10-50510152025-0.04-0.03-0.02-0.0100.010.020.03Shear Stress(MPa)Shear Strain4C/min14C/minlifetime under TC stress can be modeled by Clechs“board thickness”model 41.Clechs model is developed by modeling the mechanical coupling between component and PCB from first principles.Although it is commonly referred to as the“board thickness”model,it is a comprehensive model that accounts for all critical parameters involving the component,board,and assembly.Based on Clechs model,the overall lifetime,NTotal,consists of three parts of life which associate three different mechanical coupling mechanisms.The first part,N1,is the lifetime that is characterized by the in-plane tensile shear force,acting on the device.Figure 4-38 illustrates the evolution of the dimensional changes of a device and a PCB when the ambient temperature increases from a low temperature,where the stress on the solder joints is neutral,to the hot temperature extreme where the device expands significantly less than the PCB due to the CTE mismatch.As a result,the solder joints are stretched laterally as shown in Figure 4-38.N1 represents the in-plane tensile stiffness of the mounted device as shown by the green arrow in Figure 4-38.Eq.4-28 specifies the lifetime caused by such in-plane stencil shear force.where F is a constant for a specific device-PCB system and under a given TC stress condition,is the CTE mismatch between the device and PCB,Dev is the Poissons ratio of the device,EDev is its Youngs modulus,and hDev is the height of the device.C1 is denoted as the axial compliance of the device,The second term,N2,is controlled by the in-plane tensile shear force that acts on the PCB as highlighted by the yellow arrow in Figure 4-38.Eq.4-29 characterizes the corresponding lifetime that is related to such tensile stiffness of the PCB.where F and are the same as in Eq.4-28,PCB is the Poissons ratio of the PCB,EPCB is its Youngs modulus,and hPCB is the PCB thickness.C2 is defined as the axial compliance of the PCB,.Eq.4-28.Stress NeutralDeviceDeviceTemperatureRisesPCBPCBIn-planeShear Force(PCB)In-planeShear Force(Device)High TemperatureEq.4-29 RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|26Figure 4-39 COMSOL FEA simulation results illustrate the flexural bending between the device and PCB.Lastly,N3 represents the bending moments of the bimetallic strip of the device and PCB,as shown in Eq.4-30.Figure 4-39 shows the FEA simulation result of such bending motion.This part of lifetime,N3,is dominated by the flexural modulus of the device and the PCB.Where and are the flexural Youngs modulus of the device,respectively.C3 is the bending compliance of the bimetallic strip assembly of the device and PCB,and H is further defined by Eq.4-31.where hStandoff is the standoff height of the solder joint post-assembly.Therefore,the total lifetime NTotal is determined by the sum of all three parts,as shown in Eq.4-32.Previous reliability reports showed that N3,representing the bending motion interacting between the device and the PCB,dominates the total lifetime,NTotal 47.Since the hPCB used in high-power density applications is significantly thicker than both hDev and hStandoff,H is essentially equal to hPCB.Therefore,NTotal can be simplified to Eq.4-33.where A,B and C are constants that depend on the material properties of the PCB,the device and the solder joints post-assembly.Eq.4-33 suggests that the TC lifetime is inversely proportional to the PCB thickness,assuming all other parameters remain constant as the PCB thickness decreases.Similar accelerated TC experiment was conducted on EPC2218(identical to EPC2218A in package)mounted on a 16-copper-layer PCB with a total thickness of 3.2 mm.The TC test conditions and assembly of the 16-layer PCB were consistent with those of the 2-copper-layer PCBs,with TC1 condition:-40C to 125C.Weibull distribution analysis found that the MTTF of the 16-layer Eq.4-30DevPCBEq.4-31Eq.4-32Eq.4-33in TC experiment decreased by approximately 40%compared to the MTTF of the 2-layer PCB,which is consist with the projection from the model in Eq.4-33.Figure 4-40 shows the TC lifetime extrapolation from Eq.4-33 as a function of the number of the copper layers within the PCB,based on three assumptions.First,the PCB thickness scales linearly with the number of copper layer with the copper thickness per layer being two Oz,approximately 70 m.Second,the prepreg material is made of standard FR4 with a CTE of 18 ppm/C.Lastly,the modulus and CTE mismatch between device and PCB remain constant as the number of copper layers decreases,indicating the total Cu/FR4 ratio stays consistent.0246810121416180.50.60.70.80.91.01.11.2Normalized TC LifetimeNumber of Cu layers in PCBFigure 4-40:TC Lifetime vs.number of copper layer in PCB,where all MTTF is normalized to MTTF of 2 Cu-layers PCB.4.4.3.Criteria for Choosing a Suitable Underfill The selection of underfill material should consider a few key properties of the material as well as the die and solder interconnections.First,the glass transition temperature of the underfill material should be higher than the maximum operating temperature in application.Also,the CTE of the underfill needs to be as close as possible to that of the solder since both will need to expand/contract at the same rate to avoid additional tensile/compressive stress in the solder joints.As a reference,typical lead-free SAC305 and Sn63/Pb37 have CTEs of approximately 23 ppm/C.Note that when operating above the glass transition temperature(Tg),the CTE increases drastically.Besides Tg,and CTE,the Young(or Storage)Modulus is also important.A very stiff underfill can help reduce the shear stress in the solder bump,but it increases the stress at the corner of the device,as it will be shown later in this section.Low viscosity(to improve underfill flow under the die)and high thermal conductivity are also desirable properties.RELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|27Table 4-7:Recommended underfill material properties for WLCSP GaN devicesFigure 4-41:Simulation deck for finite element analysis of stresses inside EPC2206 under temp cycling stress.Die with underfill sitting on 1.6 mm FR4 PCB.Stress is analyzed along cut line shown.Figure 4-42:Von Mises(peak shear stress)in the edge-most solder bar under a temperature cycle change of T= 100C.Four different underfill conditions are simulated,with changing Youngs modulus(E)of the underfill,and different CTE as well.Note that mechanical deformation has been exaggerated by 20 x in all cases.ManufacturerPart numberCTE(ppm/C)Storage modulus(DMA)at 25C (N/mm2)Viscosity at 25CPoissons RatioVolumeResistivityThermalConductivityDielectricStrengthTg(TMA)CBelowTgAboveTgHENKELS LOCTITEECCOBOND-UF 11731602610360007.5 Pa*SNAMICSU8437-213732100850040 Pa*S0.331E15-cm 0.67 W/m KNAMCISXS8410-40613819701300030 Pa*SThe main guidelines for choosing an underfill for use with GaN transistors are listed below:Underfill CTE should be in the range of 16 to 32 ppm/C,centered around the CTE of the solder joint(24 ppm/C).Lower values within this range are preferred because they provide better matching to the die and PCB.Glass transition temperature(Tg)should be comfortably above the maximum operating temperature.When operated above Tg,the underfill loses its stiffness and ceases to protect the solder joint.Youngs(or Storage)modulus in the range of 613 GPa.If the modulus is too low,the underfill is compliant and does not relieve stress from the solder joints.If it is too high,the high stresses begin to concentrate at the die edges.To better understand the key factors influencing thermo-mechanical reliability when using underfills,finite element simulations of EPC2206 under temperature cycling stress were conducted.Figure 4-41 shows the simulation deck used for this analysis.The die is placed on a 1.6 mm FR4 PCB,and the temperature change is T= 100C above the neutral(stress free)state.Two key underfill parameters were varied:Youngs modulus and CTE.As shown in the figure,stress is analyzed along the cut line shown,providing visibility into the stress within the solder bars,die,and underfill.Figure 4-42 shows the von Mises 58 peak shear stress in the edge-most solder bar along the cutline.For clarity,only stress in the solder bar is shown.In addition,mechanical deformations are exaggerated by 20 times in order to illustrate the shear displacement in the joint.Four distinct underfill conditions are simulated by changing the Youngs modulus(E)or the CTE of the underfill.As can be seen,the solder bar in the no underfill case has by far the most extreme shear stress and deformation.The addition of underfill significantly alleviates stress from the joint.Higher Youngs modulus reduces this stress further.For underfills with poor CTE matching to the solder joint,stress can also build up in the joint.Figure 4-43 shows the same four conditions,but this time the von Mises stress is shown in both the die and underfill.The high Youngs modulus cases show low stress in the solder joint,but high stress inside the die and underfill near the die edge.These high stresses can lead to cracking and ultimate failure inside the device.FEA analysis shows that there is an optimal Youngs modulus in the range of 6 to 13 GPa,providing a good compromise between protecting the solder joint and protecting the die edge.Regarding CTE,the analysis shows that high underfill CTE(32 ppm/C)should be avoided.EPC2206UnderfllSolder barsPCBNo UnderfllE=7 GPaCTE=23E-6/CE=20 GPaCTE=23E-6/CE=7 GPaCTE=40E-6/CT= 100Cvon Mises Stress in Edgemost SolderjointRELIABILITY REPORTPhase Seventeen TestingEPC POWER CONVERSION TECHNOLOGY LEADER|EPC-CO.COM|2025|For more information:infoepc-|28The effect of underfill on TC reliability was studied using EPC2218A 59 under the TC1 conditions of 40C to 125C,where two group of parts were compared:one with and one without underfill material.The underfill material selected was from Henkels Loctite(part number:Eccobond-UF 1173)which showed good perfor
2025-03-13
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THE PRACTICAL STRATEGIES THE E-MOTOR INDUSTRY IS TAKING TO REDUCE DEPENDENCY ON RARE EARTH MATERIALS25th28th February 2025 Munich,Germanywww.automotive- From Automotive IQ 2.Motives Behind Reducing Dependency 4.Key Considerations for Alternatives to Rare Earth Materials6.Alternative Materials and Magnets to Replace Rare Earth Materials:What Are the Key Considerations?Interview with Krishna MPK Namburi,System Controls Engineering Manager at Nexteer Automotive 9.Practical Strategies to Reduce Dependency on Rare Earth Materials Interview with Dr.Joo Bonifcio,Head of System Design,Systems Engineering Electronics and Software-Electrified Powertrain Technology at ZF Group 10.Alternative Solutions What Are Leading OEMs and Tier-1s Doing?11.Insights on HREE-Free Magnets,Powertrain Design,and Emerging Technologies Interview with Dominic Bezikofer,Head of E-motors&Components NA,Author&Initiator,ZF Group www.automotive- OF CONTENTSThe conversation surrounding the use of rare earth materials and the environmental impact and cost of using them,has led to many in the industry actively working to move away from the use of rare earth metals like cobalt and neodymium.The key challenge for the industry is finding alternatives that produce the same or more levels of efficiency as the permanent magnet motor.Just like other industries in the process of changing their design and development methods to produce products that contribute to a clean-air environment,the Automotive industry can expect a rapid rise in demand for the rare earth materials used in magnets,which are traditionally used to build electric motors.An analysis from the International Energy Agency shows that the demand for rare earth metal to be used in electric vehicles is set to sore by 2040 by nearly three times the amount in 2020.This contributes to the industrys stress of avoiding high costs and accessing supplies.Few alternatives to permanent magnets exist today.Recycling can help to reduce the need for future rare earth mining and processing,but there wont be enough used material to meet the growing demand for decades.This report will analyse the motives behind the desire to reduce the dependency on using rare earth materials,the key considerations in doing so,as well as the strategies that can be taken to achieve this goal.www.automotive- FROM AUTOMOTIVE IQ www.automotive- Behind Reducing Dependency The automotive industrys shift away from relying on rare earth materials for EVs is driven by several key motivations which collectively drive the industrys need of more sustainable and stable alternatives to rare earth materials in EV production.The shift not only addresses supply chain and environmental concerns but also leverages technological advancements to improve performance and cost-effectiveness.Supply Chain Concerns The concentration of rare earth materials primarily in China raises significant supply chain security and geopolitical risks.Disruptions or export restrictions from China can lead to supply shortages and price spikes,impacting production stability and costs for automakers(SPGlobal)(Autoblog).China continues to dominate the supply of rare earth elements(REEs),accounting for about 85-95%of global production.This dominance creates geopolitical risks,particularly as China has previously used its control over REEs as leverage in political disputes.The ongoing“tech trade war”between China and the United States exemplifies this risk,emphasising the need for alternative supply chains to reduce dependency on Chinese REEs(The Diplomat)(Mining).Efforts to diversify the production of REEs outside of China are making headway but remain complex and slow-moving.Countries like the United States and those in the European Union are actively seeking alternative sources,including Australia and parts of Europe,to ensure a stable and conflict-free supply of these critical materials.This trend underscores the importance of developing robust,diversified supply chains to mitigate geopolitical risks and ensure the sustainability of EV production(Mining)(The Diplomat).Cost Volatility The prices of rare earth materials are highly volatile due to fluctuating supply and demand dynamics,as well as geopolitical tensions.This price instability can adversely affect the profitability and economic feasibility of EV production(Autoblog).Environmental Impact The extraction and processing of rare earth materials have considerable environmental consequences,including habitat destruction,water pollution,and soil contamination.Reducing reliance on these materials aligns with the industrys sustainability goals and regulatory pressures to minimise environmental footprints(SPGlobal)(IMechE).The cyclical nature of the mining industry,economic conditions,and the increasing demand for sustainable technologies are all factors influencing the REE market.The inflationary environment and fluctuating EV demand directly impact REE prices and availability,highlighting the need for strategic planning and investment in alternative technologies and supply chains(Mining)(The Diplomat).Technological Innovation The push towards reducing reliance on REEs includes advancements in motor technologies.For example,the development of induction motors and switched reluctance motors,which do not require REEs,is gaining traction.These technological innovations are crucial in decreasing dependency on REEs and promoting sustainable practices within the industry(Data&analytics solutions)(Data&analytics solutions).Tesla is working on a permanent magnet electric motor that doesnt use rare earth metals,which is expected to lower costs and reduce environmental and health risks associated with rare earth mining(SPGlobal).With Tesla dominating the automotive market,its understandable how other manufacturers would want to follow suit.Other alternatives like ferrite magnets and inductive-excited synchronous motors are also being explored for their potential to offer similar or even superior performance without the environmental drawbacks(Autoblog)(IMechE).Regulatory Pressures Regulatory initiatives aimed at reducing dependence on rare earth materials and promoting sustainable practices are pushing the automotive industry to explore alternative solutions.Compliance with these regulations and achieving sustainability targets are increasingly critical for automakers(IMechE).Europe:In July 2023,the European Commission proposed a regulation covering the entire life cycle of vehicles,from design to disposal,to enhance the resource efficiency of the automotive sector.The regulation aims to improve vehicle design and end-of-life management by setting circularity requirements for reusability,recyclability,recoverability,and the incorporation of recycled content.It also includes mandates for the labeling and information of vehicle parts,components,and materials.Additionally,the regulation establishes criteria for extended producer responsibility,the collection and treatment of end-of-life vehicles,and the export of used vehicles outside the EU(Euro PA).The U.S.:The U.S.Environmental Protection Agency(EPA)promotes the reduction and reuse of rare earth materials in the automotive industry through voluntary consensus standards.The NSF/ANSI 426-2018 standard incentivises the use of recycled rare earth elements in product design,such as in hard disk drives and electric vehicle motors.This standard aims to mitigate environmental impacts,enhance resource efficiency,and secure a stable supply of critical materials.Companies like Dell have pioneered closed-loop recycling processes,repurposing rare earth magnets from old devices into new products,significantly reducing waste(US EPA).www.automotive- has introduced regulations to secure its supply of rare earth materials,essential for manufacturing electric vehicles and consumer electronics.These rules,effective from October 2024,govern the mining,smelting,and trade of rare earth elements,ensuring traceability and accurate recording of product flows.The regulations aim to protect national security and prevent technology export related to rare earth magnets and extraction methods.These measures are part of Chinas broader strategy to maintain its dominance in global rare earth production while addressing geopolitical tensions(Reuters).Key Considerations As the automotive industry explores alternatives to rare earth materials for electric motors,several critical factors must be evaluated to ensure that the transition meets performance and operational standards.Here are the key considerations:Costs Implications Rare earth elements(REEs)like neodymium and dysprosium,commonly used in permanent magnet motors,are expensive due to their limited supply and the complex geopolitical landscape surrounding their extraction.As manufacturers seek to avoid these costs,they are exploring alternative motor designs that do not rely on these materials.These alternatives,however,often come with their own cost challenges,particularly in terms of research and development,as well as potential modifications in manufacturing processes(IMechE)(SpringerLink).Material Costst Substitute Materials:Ferrite magnets and iron nitride magnets are potential alternatives to rare earth magnets.Ferrite magnets,for example,are less expensive than rare earth magnets,with costs around one-tenth that of rare earth alternatives.However,these materials generally offer lower magnetic strength,which may necessitate redesigns or larger components.t Production Costs:Transitioning to alternative materials involves redesigning motor components and retooling production lines,which could increase initial manufacturing costs.Its vital that the industry weighs up the initial costs of the change with the ongoing costs of supply chain demands.Performance and Efficiency Traditional motors using rare earth magnets are highly efficient and provide excellent performance,particularly in terms of power density and torque.However,new designs,such as separately excited synchronous motors(SESM)and innovative concepts like ZFs In-Rotor Inductive-Excited Synchronous Motor(I2SM),are being developed to match or even exceed the performance of conventional motors without the use of rare earth materials.These new designs aim to eliminate efficiency losses,especially in high-speed operations,and to maintain power density,thus ensuring they remain competitive alternatives(IMechE)(tulatech).Design Considerations Moving away from rare earth magnets necessitates significant design adjustments.For instance,SESM motors require additional components like brush elements and slip rings,which can complicate the motor design and affect its overall compactness.The I2SM technology addresses some of these issues by using an inductive exciter within the rotor shaft,which simplifies the design by eliminating the need for mechanical contacts,reducing space requirements,and allowing for better integration into existing vehicle architectures(IMechE).www.automotive- Materials and Magnets to Replace Rare Earth Materials:What Are the Key Considerations?Interview with:Krishna MPK Namburi,System Controls Engineering Manager at Nexteer Automotive Q:How does the performance and efficiency of traditional motor designs compare with new non-earth technologies and materials?A:Traditional motors using rare earth elements like neodymium and dysprosium exhibit high power density and efficiency due to their strong magnetic properties.However,these elements are expensive,have supply chain risks,and pose environmental challenges from mining.New motor technologies aim to reduce reliance on rare earth elements by using alternatives like ferrites,and other magnetic alloys,which are more abundant,cheaper,and environmentally friendly.Innovations in motor design,such as improved electromagnetic structures,advanced cooling techniques,and optimised control systems,are helping to close the performance gap between traditional rare earth motors and non-rare earth alternatives.While some advanced non-rare earth motors achieve comparable efficiency and performance,there may be trade-offs in specific applications.Overall,these advancements are creating more sustainable and cost-effective motor solutions.Q:Can you provide specific examples or case studies where alternative materials have demonstrated comparable or superior performance to rare earth materials?A:Most commercial FEVs(Fully Electric Vehicles),such as the BMW i3,Nissan Leaf,and Volkswagen e-Golf,use Permanent Magnet Synchronous Motors(PMSMs).While these typically rely on rare-earth magnets,there is a trend to reduce this dependency.For example,a European consortium led by Infineon developed a spoke rotor PMSM with ferrite magnets that match or exceed the power density of equivalent induction motors.Similarly,the U.S.Department of Energys FreedomCAR project is developing a spoke rotor PMSM using ferrite magnets.Also,extensive research is being conducted on a new machine topology called the Biaxial Excitation Synchronous Machine(BESM)which is classified as a hybrid excitation machine.The design incorporates both field winding and ferrite magnets on the rotor and offers comparable cost and efficiency to similar machines that use rare earth magnets.Q:What are the primary cost implications when transitioning from rare earth materials to alternative options?A:Alternatives like ferrite magnets and advanced steels are more abundant and less expensive than rare earth elements,significantly reducing material costs.Using these alternatives can enhance supply chain stability,avoiding geopolitical issues and potential disruptions associated with rare earth materials.Although initial investments in new equipment or manufacturing adjustments may be required,these costs are often offset by lower ongoing production expenses.Additionally,alternatives are more environmentally friendly,reducing potential regulatory and compliance costs,while maintaining comparable performance in most applications.Q:Are there any hidden or long-term costs associated with using alternative materials?A:Using alternative materials could involve hidden or long-term costs in terms of adoption,manufacturing and engineering time.Development and testing require significant initial investment in research,development,and validation.Transitioning to new materials necessitates manufacturing adjustments,including retooling,new equipment,and staff retraining,leading to substantial expenses.While alternatives might not match the long-term reliability and durability of rare earth materials,potentially increasing maintenance costs,establishing a new supply chain also involves costs for securing suppliers,quality control,and logistics.In general,there are several comparable alternatives in terms of performance.However,selecting the appropriate trade-off based on the application and optimizing for it would be crucial.www.automotive- you share specific design details/innovations in the development of alternative materials and magnets that have successfully replaced rare earth materials?A:Innovations in magnet design have led to alternatives like ferrite and alnico magnets,which offer strong magnetic properties and temperature stability without relying on rare earth materials.Composite and hybrid magnets,such as bonded and hybrid combinations,provide flexibility and reduce rare earth dependence.Advanced manufacturing techniques like 3D printing and improved sintering and coating methods optimise magnet performance and customisation.Innovation in the implementation of processes that minimise environmental impact and energy consumption during production is also being considered.Additionally,electric machines such as PM-assisted synchronous reluctance machines and bi-axial excitation synchronous machines use non-heavy rare earth materials.These machines achieve efficiency,torque density,and other performance metrics that are comparable to those of machines using heavy rare earth materials.This reflects ongoing efforts to diversify magnet materials,reduce dependence on rare earths,and improve overall performance and sustainability in various industries.Q:What are key challenges when designing motors without rare earth materials,and how can they be overcome?A:Designing motors without rare earth materials pose challenges,primarily related to achieving comparable magnetic performance,temperature stability,and managing costs.Alternative magnets often lack the high magnetic flux density of rare earth magnets,but hybrid magnets and advanced magnetisation techniques have helped enhance their properties.To maintain temperature stability,new alloys and composite materials have been developed,alongside efficient cooling systems.Cost and availability issues are addressed by exploring alternatives like ferrite and alnico,and by improving motor efficiency to offset material costs.Compact designs with high power-to-weight ratios are achieved through design optimisation and the incorporation of lightweight materials.Finally,environmental and sustainability concerns are tackled by establishing recycling programs and adopting green manufacturing practices.Q:What potential do wound synchronous machines or ferrite magnets hold as replacements for permanent magnet synchronous motors(PMSMs)?A:Wound synchronous machines and ferrite magnets hold significant potential as replacements for permanent magnet synchronous motors.Ferrite magnets are being extensively studied as replacements for rare earth magnets due to their lower electrical conductivity,which results in very low eddy current losses and reduced demagnetisation.However,ferrite magnets have much lower residual induction and maximum energy product than NdFeB magnets,making them unsuitable for direct replacement in permanent magnet(PM)machines because they generate less magnetic flux density.Despite these limitations,some studies claim that axial flux ferrite PM motors can be competitive in terms of torque/power density and efficiency compared to the rare-earth PM motor used in the Toyota Prius 2010 HEV.These studies suggest that PMSMs with ferrite PMs are better suited to replace rare-earth PM motors than induction motors or switched reluctance motors(SRMs),particularly in electric traction applications and in-wheel applications.Currently,most commercial FEVs use PMSMs,but there is a trend to minimise permanent magnet content due to cost and supply risks associated with rare-earth materials.Projects like the European consortium led by Infineon and the U.S.Department of Energys FreedomCAR initiative are developing spoke rotor PMSMs with ferrite magnets,aiming for similar or better power density than equivalent induction motors.Q:Are there any particular applications or industries where these alternatives are especially promising?A:Alternative materials to traditional rare-earth PMs are gaining attention in several key industries.In the automotive sector,these materials are being explored for use in electric and hybrid electric vehicles to achieve environmentally friendly and cost-effective propulsion systems.They are also considered for wind turbines,particularly in low-speed direct drive generators,due to their high efficiency and power density,which addresses concerns over the supply and cost of rare-earth elements.Additionally,alternative materials offer high efficiency and reduced costs compared to rare-earth PMs in home appliances,promoting more economical and environmentally friendly household devices.Moreover,rare-earth-free electric motors are being adopted across various sectors needing efficient and compact machines,offering advantages such as lower costs,enhanced ruggedness,and higher temperature operation compared to traditional motors.These applications underscore the potential of alternative materials to meet sustainability goals while enhancing performance in critical industrial applications.Factors That May Increase Costs Initial investment in research and development Manufacturing adjustments Potential increased maintenance costs Establishing a new supply chain Performance trade-offsFactors That Will Reduce Costs Abundance and lower cost of materials Enhanced supply chain stability Lower ongoing production expenses Environmental benefitswww.automotive- does the complexity of control systems change when using non-rare earth materials in motor design?A:When incorporating non-rare earth materials into motor design,the complexity of control systems can change significantly.These materials may possess different magnetic and thermal properties compared to rare-earth magnets,necessitating adjustments in control algorithms to optimise motor performance.Control systems must manage variations in magnetic field strength and stability effectively to ensure precise torque control and operational reliability.Additionally,differences in temperature sensitivity require the implementation of monitoring and compensation strategies to maintain optimal motor performance across varying environmental conditions.Mechanical integration considerations also come into play,as non-rare earth magnets may have distinct mechanical characteristics that impact motor assembly and operation.Balancing these factors with cost considerations,since non-rare earth materials are chosen partly for their affordability,requires careful control system design to achieve both cost-efficiency and performance objectives in motor applications.Machines like SRM,and a few others may require active torque ripple cancellation algorithms to improve the Noise,Vibration and Harshness(NVH)performance.Q:What are the technical challenges in implementing control systems for motors that use alternative materials,and how can they be overcome?A:Alternative materials often exhibit greater variability in magnetic properties compared to traditional rare-earth magnets,necessitating the development of robust control algorithms capable of adapting to these variations.Moreover,some alternative materials display non-linear magnetic characteristics,complicating the design of control strategies focused on precise speed and torque regulation.Another significant challenge arises from the potential differences in thermal behavior among alternative materials,which can impact motor performance.To overcome these challenges,control systems need to incorporate advanced temperature monitoring and compensation techniques to maintain optimal operation across varying environmental conditions.Achieving comparable levels of efficiency and power density as rare earth magnets with alternative materials demands fine-tuning of control parameters and possibly redesigning motor control strategies.Utilising advanced sensors for monitoring temperature,magnetic flux,and motor performance parameters can enhance control accuracy and reliability.Additionally,leveraging computer modeling,simulation tools,and iterative testing with prototype motors using alternative materials is crucial to predict and optimise performance under various operating conditions.Through collaborative research and innovation across disciplines,tailored control strategies can be developed to effectively harness the unique characteristics of alternative materials in motor applications,ensuring optimal performance and reliability.Q:What does the manufacturing process look like for motors based on non-rare earth materials?A:Manufacturing motors based on non-rare earth materials involves several essential steps:First,appropriate non-rare earth materials such as ferrites or Alnico are selected based on magnetic properties and cost considerations.Components like rotor and stator cores are prepared through cutting,shaping,and forming processes depending upon the type of machine topology.The magnets undergo magnetization to align their magnetic domains effectively.Assembly of motor components,including the rotor,stator,windings,bearings,shaft,and housing,follows with precise alignment and fitting.Finally,rigorous testing and quality control ensure compliance with performance standards before final assembly and packaging for distribution or installation.This process ensures that motors utilising non-rare earth materials meet stringent requirements for performance,reliability,and environmental considerations in various applications.Q:Are there any other significant differences in the manufacturing techniques required for these alternatives compared to traditional methods?A:Manufacturing motors with alternative materials such as ferrites or Alnico involves distinct techniques compared to traditional rare-earth methods.These differences extend to handling,processing,and magnetisation processes tailored to the specific characteristics of non-rare earth materials.Adjustments in component design and assembly are necessary to optimise magnetic and mechanical performance for these alternatives.Temperature sensitivity influences manufacturing choices,requiring precise heat treatment and insulation methods to ensure operational stability across different thermal conditions.Cost effectiveness and sustainability drive optimisations in material usage and processing steps,reflecting a broader commitment to efficient production practices.Finally,quality control measures are crucial to maintain consistent performance and reliability,addressing variations inherent in alternative materials and their manufacturing processes.www.automotive- Strategies to Reduce Dependency on Rare Earth Materials Interview with:Dr.Joo Bonifcio,Head of System Design,Systems Engineering Electronics and Software-Electrified Powertrain Technology at ZF Group Q:How can OEMs reduce using rare earth materials,or completely remove them from motors while maintaining efficiency?A:The reduction of rare-earth materials is a very important subject not only due to its cost impact,but also due to the possibilities of reducing the CO2 footprint of e-drives and minimising supply risks.We are currently working on two different paths for promoting the reduction or elimination of rare earth materials.The first path to take is the reduction of the heavy rare earth content of magnets in Permanent Magnet Synchronous Machines(PMSM).The other one relates to the use of magnet-free technologies,like the Separately Excited Synchronous Machines(SESM).Both paths have their own advantages and challenges,but it is clearly possible to maintain high efficiency levels on both solutions.Q:Following on from this,what low-cost material alternatives to rare earth materials are there and why are alternatives required?A:Alternatives are required due to strategic and economic aspects.Rare earth materials and the heavy rare earth elements(HREE)in particular have a significant environmental impact in their mining and processing.Moreover,there are also concerns about supply chain security and price volatility,which could drastically impact long-term perspectives for e-mobility products.For the PMSM,a first step on reaching high efficiencies and power densities with magnets free of the heavy rare earth elements Dysprosium(Dy)and Terbium(Tb)have been demonstrated in our e-drive prototype EVSys800,where a very high torque density(5210 Nm/75 kg)and 75%ratio of continuous and peak power have been reached.For the SESM,we developed the In-rotor Inductive-Excited Synchronous Machine(ISM),which features a brushless exciter fully integrated into the rotor shaft and a completely magnet-free rotor.The brushless exciter shows 15%less losses than commonly used exciter technologies and the machine has a superior power density(with up to-90mm axial length advantage compared to benchmark)and very high efficiencies in highway cycles than comparable technologies.Q:How can alternative technologies be used to reduce costs?A:These alternative technologies can have a significant cost-reduction impact enabled by using cheaper materials,i.e.,copper instead of magnets in one case and the use of cost-optimised magnets in the other.Moreover,if these technologies can be combined with a system level approach,which includes optimised cooling systems and various software functions,just to mention two examples,that can enable weight reductions and the use of less materials,the reachable cost savings can be further increased.It is also important to mention that the reduced supply-chain risks with,for instance,the ISM technology can also reduce future cost risks.Q:How can OEMs incorporate rare earth free motors into different models?A:The key for increasing the application range of magnet-free motor topologies relies on their optimisation for reaching the requested performance while minimising the necessary installation space.This was one of the main motivations for developing the ISM technology.As objectives for this product,we set the same values of the performance indicators efficiency,power,and torque as a state-of-theart PMSM e-drive system,while keeping the same installation space as the reference machine.This would allow a seamless transition between these two technologies in the available platforms.Automotive IQ spoke with Dr.Joo Bonifcio,Head of System Development Advanced Engineering-Electrified Powertrain Technology at ZF Group to learn insights on reducing rare earth materials,the strategies taken in doing so,how efficiency can be improved and what alternatives can be used.www.automotive- are the challenges related to reducing the amount of rare earth materials in motors,and more importantly,what are the solutions?A:There are multifaceted challenges at system level that must be solved for reducing the amount of rare earth materials.To mention an example of challenges we face for the PMSM,when reducing the heavy rare earth content of magnets,we also increase their temperature sensitivity and therefore the probability of demagnetisation.To cope with this challenge,the thermal conditioning of the e-drive system must be designed very carefully and the system reactions to special situations like short circuits must be considered as well.Regarding the ISM,we are also faced with similar challenges.The cooling system has a significant impact on continuous performance and on efficiency,and the functional challenges related to the technology must be addressed with a suitable system design.Q:What technologies can be used to eliminate the dependency of using rare earth materials?A:The two previously mentioned technologies have an enormous potential for reducing or eliminating the dependency of using rare earth materials.The scope of application of each one will depend on the exact requirements needed for a particular scenario.Nevertheless,they represent a big leap forward in the direction of building more cost-effective and sustainable e-drives.Alternative Solutions What Are Leading OEMs and Tier-1s Doing?Tesla Tesla announced in 2023 its intention to eliminate rare earth elements from the motors of its future electric vehicles.This decision marks a bold move toward greater sustainability and supply chain independence.Rare earth materials,such as neodymium,have been crucial in producing the powerful magnets used in EV motors.But,as we know,these materials are costly,and their extraction can cause significant environmental damage.The company is exploring the use of alternative materials and motor designs that can achieve similar or even superior performance without relying on rare earth elements.One potential approach involves switching from permanent magnet motors,which rely heavily on rare earth materials,to induction motors or newly designed synchronous motors that do not require these elements.Tesla has also hinted at the possibility of leveraging new magnetic materials that are more abundant and environmentally friendly.At Teslas investor day in March 2024,Teslas Director of Power-Train Engineering,Colin Campbell,stated that Tesla has designed its next drive unit,which uses a permanent-magnet motor,which does not use any rare-earth materials(Spectrum).The rest of the industry does not seem convinced that this is achievable.A quote from Alexander Gabay,a researcher at the University of Delaware,proving this doubt:“I am sceptical that any non-rare-earth permanent magnet could be used in a synchronous traction motor in the near future.”Teslas transition away from rare earth materials is not without challenges.One significant hurdle is maintaining the performance and efficiency of their vehicles.Rare earth magnets are known for their ability to produce strong magnetic fields with relatively little material,contributing to the high-power density and efficiency of Teslas current motors.Replacing these magnets with alternative materials or motor designs could lead to a decrease in these performance metrics unless new technological advancements are made,for which,time will tell.Moreover,the development of these new technologies will require substantial research and innovation.Tesla may have announced its going to make these changes,but realistically,can this be achieved?Tesla must overcome both technical challenges,such as finding or engineering suitable materials,and economic challenges,including scaling up production and integrating these new technologies into their existing manufacturing processes.ZFs Magnet-Free Electric Motor In response to the automotive industrys search for alternatives to rare earth materials in E-Motor technologies,ZF has pioneered a groundbreaking solution:the magnet-free electric motor.This innovative approach marks a significant shift from traditional motor designs that rely on rare earth magnets,offering a promising avenue for reducing dependency on these materials(ZF Press Center).The magnet-free electric motor developed by ZF utilises a novel interior permanent magnet synchronous motor(IPMSM)design.Unlike conventional motors that incorporate rare earth magnets within their rotor structures,ZFs motor achieves magnetic field generation through a unique configuration of winding arrangements and materials.By leveraging advanced materials and engineering techniques,ZF has effectively eliminated the need for rare earth magnets while maintaining optimal motor performance.In a press release for ZF Group,CEO,Dr.Holger Klein,stated:“With this magnet-free e-motor without rare earth materials,we have another innovation with which we are consistently improving our electric drive portfolio to create even more sustainable,efficient and resource-saving mobility.This is our guiding principle for all new products.And we currently see no competitor that masters this technology as well as ZF.”www.automotive- development represents a significant advancement in the quest for sustainable and resource-efficient electric propulsion systems.By reducing reliance on rare earth materials,ZFs magnet-free electric motor addresses key challenges faced by the automotive industry,including supply chain vulnerabilities,cost volatility,and environmental concerns associated with traditional motor designs.ZFs innovation underscores the broader trend within the automotive industry towards developing alternative solutions to using magnets and rare earth materials in E-Motor technologies.Companies across the sector are actively exploring various approaches,including the adoption of alternative magnet materials,such as ferrite magnets,as well as magnet-free motor designs like ZFs IPMSM.Moreover,collaborative research efforts between industry stakeholders,academic institutions,and government agencies are driving advancements in material science,motor design,and manufacturing processes.These collaborative endeavours aim to accelerate the development and commercialisation of innovative solutions that enhance the efficiency,sustainability,and competitiveness of electric propulsion systems.Q:What is the controversy in using heavy rare-earth materials(HREE),and therefore,what are the reasons to use HREE-free magnets?A:The controversy surrounding the use of heavy rare-earth materials(HREE)involves issues related to mining,geopolitical challenges,and sustainability concerns.There is a strong desire to become independent from China and other geopolitical influences,which is why there is a push to use HREE-free magnets.Q:What challenges come with using HREE-free magnets,and what are the solutions to those problems?A:One of the main challenges with using HREE-free magnets is their lower temperature resistance,which can lead to potential demagnetization in very warm conditions.Solutions to these problems include developing new technologies,avoiding situations where excessive heat might occur,and improving cooling systems.Q:What system approach can be taken to optimise the powertrain design?A:The optimisation of powertrain design begins with clearly defining the requirements and use cases.From there,it involves focusing on efficiency,material selection,innovative design,software functionalities,and cooling systems.Q:At the upcoming E-Motor Technologies USA 2024 conference,ne of the points on the agenda from your presentation is A compact SESM rotor design due to an In-Rotor Inductive Exciter;can you elaborate on what this is and give an overview of using a compact SESM rotor design?A:The inductive design that ZF is implementing places the exciter within the rotor,as opposed to a conductive design where the exciter is outside the rotor.This inductive design saves significant installation space,approximately 90mm.INTERVIEWInsights on HREE-Free Magnets,Powertrain Design,and Emerging Technologies Interview with:Dominic Bezikofer,Head of E-motors&Components NA,Author&Initiator,ZF Groupwww.automotive- performance and efficiency results have you seen from traditional motor designs with new technology/materials that trade off the rare earth materials?A:The performance and efficiency results can vary depending on the design and technology used.In some cases,we see similar or even better results,such as when comparing PSM(Permanent Magnet Synchronous Motors)to SESM(Synchronous Excited Synchronous Motors).Ultimately,the performance depends heavily on the specific vehicle use case.Q:What costs need to be accounted for when trading off rare earth materials for other alternatives?A:When opting for alternatives to rare earth materials,potential costs to consider include the use of additional materials like copper,changes in manufacturing processes,and the need for additional investments.Q:What needs to be included,or what factors contribute to the design of the e-motor when using alternative materials and magnets?A:Factors that contribute to the design of the e-motor when using alternative materials and magnets include the installation space,specific requirements,use cases,and cooling systems.These considerations are similar to those mentioned in the earlier discussion about powertrain design.Q:What does the manufacturing process look like for a non-rare earth-based motor?A:The manufacturing process varies depending on the type of non-rare earth motor being produced.For example,the processes for an ASM(Asynchronous Motor)and an SESM are different due to the distinct technologies involved.Q:Some companies are exploring using wound synchronous machines or ferrite magnets as possible replacements for PMSMs.Whats your take on this?A:ZFs approach involves using wound synchronous machines,specifically the SESM.Ferrite magnets are also a potential solution.The future development of these technologies will reveal more about their viability.I believe we need to remain open-minded,as there isnt a single e-motor technology that will dominate;multiple approaches will likely coexist.25th28th February 2025 Munich,Germany Europes original and longest-running Advanced E-Motor Technologies conference is returning to Munich Germany,delivering solutions to the industry greatest challenges and focusing on five major drivers including:1)Sustainability,increasing efficiency and performance 2)Achieving double-digit cost reduction3)Higher energy and torque density4)Smaller packaging5)Taking e-motor speed to new levelsDOWNLOAD AGENDAREGISTER HERE
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