Restructuring TCAD System: Teaching Traditional TCAD New Tricks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 19 Apr 2022.
Builds on
6 of 17 references.
References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 51,950
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,340
- 2018Group Normalizationcited by 1,518
- 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 304
- 2020Time-Reversal Symmetric ODE Networkcited by 19
- 2021A Novel Approach for Semiconductor Etching Process with Inductive Biasescited by 3
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.