Real-Time TCAD: a new paradigm for TCAD in the artificial intelligence era
Sanghoon Myung, Jinwoo Kim, Yongwoo Jeon, Wonik Jang, In Young Huh, Jaemin Kim, Songyi Han, Kanghyun Baek, Jisu Ryu, Yoon‐Suk Kim, Jiseong Doh, Jae-Ho Kim, Changwook Jeong, Dae Sin Kim
Abstract
This paper presents a novel approach to enable real-time device simulation and optimization. State-of-the-art algorithms which can describe semiconductor domain are adopted to train deep learning models whose input and output are process condition and doping profile / electrical characteristic, respectively. Our framework enables to update automatically deep learning models by estimating the uncertainty of the model prediction. Our Real-Time TCAD framework is validated on 130nm processes for display driver integration circuit (DDI), and 1) prediction time was 530,000 times faster than conventional TCAD, and time spent for process optimization was reduced by 300,000 times compared to human expert, 2) the model achieved average accuracy of 99% compared to TCAD simulation results, and thus, 3) process development time for DDI was reduced by 8 weeks.