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讲座信息:Machine Learning for Lithography and Physical Design
发表时间:2018-03-12 阅读次数:146次

 

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讲座信息

   

Machine Learning for Lithography and Physical Design

David Z. Pan(IEEE Fellow)
Department of Electrical and Computer Engineering
The University of Texas at Austin, TX 78712

 

时间:2018年3月13日10:00-11:00
地点:张江校区微电子楼369室

 

Abstract
Machine learning is a powerful computer science technique that can derive knowledge from big data and make predictions or even decisions. Since nanometer integrated circuits (IC) and manufacturing have extremely high complexity and gigantic data, there is great opportunity to apply and adapt various machine learning (ML) techniques in IC physical verification and synthesis. In this talk, I will first give an introduction on machine learning, and then discuss several case studies, including lithography hotspot detection, lithography modeling, ML based optical proximity correction (OPC), ML based sub-resolution assist feature (SRAF) insertion, and ML based physical design. I will further discuss some challenges and research directions.

 

Biography
David Z. Pan received his PhD degree in Computer Science from UCLA in 2000. He was a Research Staff Member at IBM T. J. Watson Research Center from 2000 to 2003. He is currently Engineering Foundation Professor at the Department of Electrical and Computer Engineering, University of Texas at Austin. He has published over 280 refereed journal/conference papers and 8 US patents, and graduated over 20 PhD students. He has served in many premier journal editorial boards and conference committees, including various leadership roles. He has received a number of awards, including the SRC Technical Excellence Award (2013), 14 Best Paper Awards, DAC Top 10 Author Award in Fifth Decade (2013), DAC Prolific Author Award (2013), ASP-DAC Frequently Cited Author Award (2015), Communications of ACM Research Highlights (2014), ACM/SIGDA Outstanding New Faculty Award (2005), NSF CAREER Award (2007), SRC Inventor Recognition Award three times, IBM Faculty Award four times, UT Austin RAISE Faculty Excellence Award (2014), many international CAD contest awards, among others. He is a Fellow of IEEE and SPIE.

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