Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.
翻译:传统的TCAD模拟成功地预测和优化了设备性能;然而,它仍然面临着巨大的挑战----高昂的计算成本。已经多次尝试用深层学习取代TCAD,但还没有完全替换。本文介绍了一种新颖的算法重组传统的TCAD系统。拟议的算法预测了三维(3-D)实时的TCAD模拟,同时捕捉了差异,使深层学习和TCAD能够相互补充,并完全解决汇合错误。