Restructuring TCAD System: Teaching Traditional TCAD New Tricks
This addresses a bottleneck in device performance optimization for semiconductor design, though it appears incremental as it builds on existing TCAD and deep learning methods.
The paper tackles the high computational cost of traditional TCAD simulation by proposing a novel algorithm that restructures the system to enable real-time 3D prediction with variance capture, complementing deep learning and TCAD while resolving convergence errors.
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.