Abstract

To address the critical challenges of poor design reusability and prolonged design cycle in radio frequency integrated circuit (RFIC) designs, a modular design and optimization framework is herein proposed with inspiration from the digital design methodology. For a given fabrication process node, standardized RF library cells are first constructed by virtue of an artificial intelligence (AI)-based modeling technique. Then, the set of library cells can be reused across multiple RF designs on the same process node. The AI-based library cells are trained on accurate full-wave electromagnetic (EM) data across a broad sweep of design parameters, thereby leading to parametric RF models that are virtually as accurate as the full-wave EM data. The standardized RF library cells are reusable building blocks for the construction of RF circuits, while parametric RF models provide fast and accurate performance predictions, enabling efficient design-space exploration and optimization. The parametric RF models can deliver accurate EM data within seconds, eliminating the need for time-consuming full-wave simulations at each iteration of the design optimization. This revolutionary approach to RFIC designs boosts the design efficiency by hundreds to thousands of times. As case studies, this proposed method is applied to design integrated passive device (IPD) chips. It shows that this approach can significantly improve the design efficiency and shorten the design cycle.

Schedule

• Talk 1: EMOptimizer®: An AI-Driven Modular Design and Optimization Framework for RFICs (by Prof. Gaofeng Wang)
• Talk 2: AI-Driven Modular Designs and Optimizations of Integrated Passive Devices by EMOptimizer® (by Prof. Yazi Cao)

Prof. Gaofeng Wang

Hangzhou Dianzi University

Talk Title: EMOptimizer®: An AI-Driven Modular Design and Optimization Framework for RFICs

Bio: Gaofeng Wang received the Ph.D. degree in electrical engineering from the University of Wisconsin, Milwaukee, in 1993 and the Ph.D. degree in scientific computing from Stanford University, Stanford, California, in 2001. From 1993 to 1996, he was a Scientist at Tanner Research Inc., Pasadena, CA. From 1996 to 2001, he was a Principal Research and Development Engineer at Synopsys Inc., Mountain View, CA. In the summer of 1999, he served as a Consultant at Bell Laboratories, Murray Hill, NJ. From 2001 to 2003, he was Chief Technology Officer at Intpax, Inc., San Jose, CA. From 2004 to 2010, he was Chief Technology Officer at Siargo Inc., Santa Clara, CA. From 2010 to 2013, he was Chief Scientist at Lorentz Solution, Inc., Santa Clara, CA. From 2004 to 2013, he was also the Founding Dean and an Adjunct Professor at the Institute of Microelectronics & Information Technology, Wuhan University, Hubei, China. Since 2013, he has been a Distinguished Professor at the School of Electronics and Information, Hangzhou Dianzi University, Zhejiang, China. He has authored or co-authored 3 books and over 700 technical papers, including over 480 articles in peer-reviewed journals (such as Nature Photonics, Advanced Science, Biosensors and Bioelectronics, and IEEE Transactions), and is the holder of over 250 patents. He has been listed among the world's top 2% scientists by Stanford University since 2019.

Prof. Yazi Cao

Hangzhou Dianzi University

Talk Title: AI-Driven Modular Designs and Optimizations of Integrated Passive Devices by EMOptimizer®

Bio:Yazi Cao received the Ph.D. degree in electrical engineering from Wuhan University in 2008. He is a Distinguished Associate Professor at Hangzhou Dianzi University and an Adjunct Professor at Carleton University in Canada. He previously served for over 10 years as an Engineering R&D Design Director at a renowned overseas communications company. He is an IEEE Senior Member and a recipient of the Hangzhou Global 521 Talent Program. He has published >50 SCI-indexed papers, accumulating over 700 SCI citations and an H-index above 20, holds more than 30 authorized invention patents, and has also published a monograph titled RF Integrated Passive Device Design. He received the Hubei Province Outstanding Doctoral Dissertation Award in 2010, was a core member of the Zhejiang Province Leading Entrepreneurial and Innovation Team Award in 2019.