[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121852-en":3,"doc-seo-121852-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121852,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Approaches for VLSI Reliability Analysis - Dissertation Abstract","The reliability of Very Large Scale Integration (VLSI) circuits is critical to modern electronic devices, where electromigration (EM), time-dependent dielectric breakdown (TDDB), and temperature variation can trigger failure and shorten lifetime. Conventional physics-based modeling and simulation are accurate but computationally intensive and time-consuming. This dissertation develops machine learning methods for EM stress prediction, post-silicon thermal map estimation, and electrostatics analysis, aiming to accelerate reliability analysis while maintaining acceptable accuracy.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nMachine Learning Approaches for VLSI Reliability Analysis  \nPermalink  \n[https://escholarship.org/uc/item/202522tm](https://escholarship.org/uc/item/202522tm)  \nAuthor  \nJin, Wentian  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nMachine Learning Approaches for VLSI Reliability Analysis  \nA Dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nElectrical Engineering  \nby  \nWentian Jin  \nMarch 2023  \nDissertation Committee:  \nDr. Sheldon X.-D. Tan, Chairperson  \nDr. Shaolei Ren  \nDr. Daniel Wong  \nCopyright by Wentian Jin 2023  \nThe Dissertation of Wentian Jin is approved:  \nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nThis dissertation would not have been possible without the help and support from numerous individuals who have contributed in various ways.  \nFirst and foremost, I would like to thank my advisor, Dr. Sheldon Tan, for his invaluable guidance, support, and mentorship throughout my PhD journey. His expertise, knowledge, and dedication have been instrumental in shaping my research work and academic career.  \nI am also grateful to my committee members, Dr. Shaolei Ren and Dr. Daniel Wong, for their constructive feedback, insightful suggestions, and valuable contributions to my research. Their guidance and mentorship have been instrumental in shaping the direction of my research and ensuring its quality.  \nFurthermore, I would like to express my sincere appreciation to my fellow researchers at the VLSI Systems and Computation lab for their help and support. Especially, I want to thank Chase Cook, Zeyu Sun, Han Zhou, Shaoyi Peng, Sheri􀀋 Sadiqbatcha, Liang Chen, Jinwei Zhang, Shuyuan Yu, Yibo Liu, Maliha Tasnim, Mohammadamir Kavousi, Subed Lamichhane, Jincong Lu, Sachin Sachdeva and Chinmay Raje. Their insights, feedback, and discussions have been critical in shaping my research and broadening my perspective. I am grateful for the opportunity to work with such a talented and inspiring group of researchers.  \nLast and most importantly, I would like to thank my family, especially my wife Yirong, my father Yaping, and my mother Aihua, for their love, support, and encouragement. Their unwavering belief in me and my abilities has been a constant source of  \nmotivation throughout my PhD journey. I am grateful for their sacri􀀌ces, understanding, and patience during this challenging time.  \nOnce again, I extend my heartfelt gratitude to all those who have helped me in achieving this milestone. Thank you.  \nThe content of this thesis is reprinted or rewritten from these published materials:  \n􀀏 Wentian Jin, Sheri􀀋 Sadiqbatcha, Zeyu Sun, Han Zhou, and Sheldon X-D Tan. \\Emgan: Data-driven fast stress analysis for multi-segment interconnects\". Proceedings of the 2020 IEEE 38th International Conference on Computer Design (Chapter 2)  \n􀀏 Wentian Jin, Liang Chen, Sheri􀀋 Sadiqbatcha, Shaoyi Peng, and Sheldon X-D Tan.\\Emgraph: Fast learning-based electromigration analysis for multi-segment interconnect using graph convolution networks\". Proceedings of the 2021 58th ACM/IEEE Design Automation Conference (Chapter 2)  \n􀀏 Wentian Jin, Liang Chen, Subed Lamichhane, Mohammadamir Kavousi, and Sheldon X-D Tan. \\HierPINN-EM: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnects Using Hierarchical Physics-Informed Neural Network\". Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (Chapter 3)  \n􀀏 Wentian Jin, Sheri􀀋 Sadiqbatcha, Jinwei Zhang, and Sheldon X-D Tan. \\Full-Chip Thermal Map Estimation for Commercial Multi-Core CPUs with Generative Adversarial Learning\". Proceedings of the 2020 IEEE/ACM International Conference On Computer Aided Design (Chapter 4)  \n􀀏 We","cbCaigtTRBqTuilz","https://ap.wps.com/l/cbCaigtTRBqTuilz","pdf",29131884,1,151,"English","en",105,"# Abstract\n## Reliability challenges in VLSI circuits\n## Traditional physics-based analysis\n## Machine learning approaches for reliability\n## Dissertation scope: EM, thermal maps, electrostatics","[{\"question\":\"Why is VLSI reliability analysis important?\",\"answer\":\"VLSI circuits underpin modern electronic devices, and reliability issues like EM, TDDB, and temperature variation can cause failure and reduce device lifetime.\"},{\"question\":\"What limitation do physics-based VLSI reliability methods have?\",\"answer\":\"Physics-based models and simulators are computationally intensive and can be time-consuming to run.\"},{\"question\":\"Which reliability topics does this thesis address using machine learning?\",\"answer\":\"It focuses on machine learning for electromigration (EM) stress analysis, post-silicon thermal map estimation, and electrostatics analysis.\"}]","Machine Learning Approaches for VLSI Reliability Analysis - 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