报告题目：VLSI Mask Optimization: From Shallow To Deep Learning
报告主讲人：香港中文大学余备教授
报告时间：2022年12月22日（周四）下午4:00-6:30
报告地点：腾讯会议ID: 852-829-185
报告摘要：
The continued scaling of integrated circuit technologies, along with the increased design complexity, has exacerbated the challenges associated with manufacturability and yield. In today’s semiconductor manufacturing, lithography plays a fundamental role in printing design patterns on silicon. However, the growing complexity and variation of the manufacturing process have tremendously increased the lithography modeling and simulation cost. Both the role and the cost of mask optimization–now indispensable in the design process – have increased. Parallel to these developments are the recent advancements in machine learning which have provided a far-reaching data-driven perspective for problem solving. In this talk, we shed light on the recent deep learning based approaches that have provided a new lens to examine traditional mask optimization challenges. We present hotspot detection techniques, leveraging advanced learning paradigms, which have demonstrated unprecedented efficiency. Moreover, we demonstrate the role deep learning can play in optical proximity correction (OPC) by presenting its successful application in our full-stack mask optimization framework.
报告人简介：
Bei Yu is currently an Associate Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received the Ph.D degree from Electrical and Computer Engineering, University of Texas at Austin, USA in 2014. His current research interests include machine learning with applications in EDA and computer vision. He has served as TPC Chair of 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), served in the program committees of DAC, ICCAD, DATE, ASPDAC, ISPD, the editorial boards of ACM Transactions on Design Automation of Electronic Systems (TODAES), Integration, the VLSI Journal. He is Editor of IEEE TCCPS Newsletter.
Prof.Yu received nine Best Paper Awards from DATE 2022, ICCAD 2021& 2013, ASPDAC 2021& 2012, ICTAI 2019, Integration, the VLSI Journal in 2018, ISPD 2017, SPIE Advanced Lithography Conference 2016, six other Best Paper Award Nominations (DATE 2021, ICCAD 2020, ASPDAC 2019, DAC 2014, ASPDAC 2013, and ICCAD 2011), six ICCAD/ISPD contest awards, ACM SIGDA Meritorious Service Award, and IEEE CEDA Ernest S.Kuh Early Career Award.
