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Lawrence Berkeley National Laboratory
Berkeley, CA, USA
Status
- Associate Editor, J-MMCT Editors, J-MMCT, Publications**
- Member, AI and Machine Learning Based Technologies for Microwaves Working Group, Technical Coordination & Future Directions Committee, Standing Committees**
- Member, TC-13 MICROWAVE CONTROL TECHNIQUES, Technical Committees**
- Member, TC-1 FIELD THEORY AND COMPUTATIONAL EM, Technical Committees**
- Speakers bureau, TC-13 MICROWAVE CONTROL TECHNIQUES, Technical Committees**
Biography
Zhi (Jackie) Yao (S’14, M’17) is the 2019 Luis W. Alvarez Postdoctoral Fellow with Lawrence Berkeley National Laboratory. Her research interests include large-scale HPC modeling and simulations, numerical modeling of quantum computing hardware, multiphysics interactions of electromagnetics, spintronics, etc., nonlinear magnetics modeling, novel material applications for RF frontend design, and multiferroics in RF applications. Jackie received the B.S. degree in optical engineering from Zhejiang University, Hangzhou, China, in 2012, and the M.S. and Ph.D. degrees from the University of California, Los Angeles (UCLA), in 2014 and 2017, respectively, under the mentorship of Prof. Yuanxun Ethan Wang.
Presentations
Exascale-Enabled Physical Modeling for Next-Generation Microelectronics
We have been observing unprecedented prosperity of modern electronics, thanks to scientists’ and engineers’ wise manipulation of the electromagnetic (EM) wave behavior in materials. Recently, continuous scaling down of circuitry has entailed novel wave-material interactions, most of which involve quantum effects. Moreover, such wave-material interactions are not restricted to EM wave and conventional single-phase materials. However, booming post-CMOS technologies still utilize trial-and-error development strategies due to the lack of adequate simulation tools.
I propose to develop a new exascale-ready, multiscale software framework for physical modeling of EM signals with the flexibility of incorporating additional physics coupling, targeted at current and next-generation microelectronic devices. This platform is built on leadership class GPU and supercomputing architectures, offering orders-of-magnitude speedup over existing capabilities. This effort leverages the expertise developed in collaboration with the Exascale Computing Project (ECP) Co-Design Center, AMReX, with features of massively parallel computing and block-structured adaptive mesh refinement (AMR). With the full capability to explore wave-material interactions, I also aim to promote innovative electronic devices that can have broad applications in space communication, medical diagnosis, etc.