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AI—使用设计信息推动检测和诊断.pdf

上传人: 哆哆 编号:631000 2025-04-19 26页 2.76MB

1、Executive ConferenceArtificialArtificialIntelligenceIntelligenceexplore the power of AI to transform semiconductor design&manufacturingMichael Yu/Thomas ZanonMichael Yu/Thomas ZanonPDF SolutionsPresentationPresentationAI to Use Semiconductor Design Information to Drive Inspection and DiagnosticsMarc

2、 HutnerMarc HutnerSiemensThis presentation and discussions resulting from it may include future product features or fixes,or the expected timing of future releases.This information is intended only to highlight areas of possible future development and current prioritizations.Nothing in this presenta

3、tion or the discussions stemming from it are a commitment to any future release,new product features or fixes,or the timing of any releases.Actual future releases may or may not include these product features or fixes,and changes to any roadmap or timeline are at the sole discretion of PDF Solutions

4、,Inc.and may be made without any requirement for updating.For information on current prioritizations and intended future features or fixes,contact .PDF Solutions,Exensio,CV,Cimetrix,the PDF Solutions logo,and the Cimetrix logo are registered trademarks of PDF Solutions,Inc.or its subsidiaries.All ot

5、her trademarks cited in this document are the property of their respective owners.Exensio visualizations Powered by TIBCO.2024 PDF Solutions,Inc.or its subsidiaries.All rights reserved.Copyright PDF Solutions 2024BackgroundBackgroundOne of the challenges for AI/ML application for semiconductor is th

6、e need of large data One of the challenges for AI/ML application for semiconductor is the need of large data volume,while we could benefit from utilize the model at initial stage(where there is few volume,while we could benefit from utilize the model at initial stage(where there is few data)data)In

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本文主要探讨了人工智能在半导体设计制造中的应用。文中提到,AI能够利用半导体设计信息来驱动检测和诊断,尤其是在数据量较小的初期阶段。通过案例展示,如Siemens EDA的Tessent与PDF Solutions的Exensio整合,FIRE AI算法能够总结设计几何、电气和预期电子束特性,优化扫描点,提高eProbe硬件扫描能力。此外,AI还能更有效地进行随机缺陷检测和系统故障模式覆盖,提升扫描效率和诊断准确性。关键数据包括:AI能够将类似模式的失败归类为“模式家族”,自动识别和分类缺陷,简化工程分析,以及通过学习eProbe行为优化扫描位置。最后,文章强调了将Tessent扫描诊断整合到Exensio中的重要性,以增强 yield 分析并利用先进的人工智能功能。
"AI如何提升半导体检测效率?" "如何利用AI进行半导体设计信息驱动的检测与诊断?" "AI在半导体制造中的作用和未来发展趋势是什么?"
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