论文标题
重组TCAD系统：传统TCAD新技巧
Restructuring TCAD System: Teaching Traditional TCAD New Tricks
论文作者
论文摘要
传统的TCAD模拟成功地预测和优化了设备性能。但是，它仍然面临着巨大的挑战 - 高计算成本。有很多尝试用深度学习代替TCAD的尝试，但尚未完全替代。本文提出了一种新型算法进行了重组，该算法进行了传统的TCAD系统。所提出的算法可以实时预测三维（3-D）TCAD模拟，同时捕获差异，使深度学习和TCAD相互补充，并完全解决融合错误。
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.
