Application of Deep Learning in Back-End Simulation: Challenges and Opportunities
Yufei Chen, Haojie Pei, Xiao Dong, Zhou Jin, Cheng Zhuo
Abstract
Relentless semiconductor scaling and ever increasing device integration have resulted in the exponentially growing size of the back-end design, which makes back-end simulation very time- and resource-consuming. With the success in the computer vision community, deep learning seems a promising alternative to assist the back-end simulation. However, unlike computer vision tasks, most back-end simulation problems are mathematically and physically well-defined, e.g., power delivery network sign off and post-layout circuit simulation. It then brings broad interests in the community where and how to deploy deep learning in the back-end simulation flows. This paper discusses a few challenges that the deployment of deep learning models in back-end simulation have to confront and the corresponding opportunities for future research.