# Restructuring TCAD System: Teaching Traditional TCAD New Tricks

arXiv: https://arxiv.org/abs/2204.09578 · HTML: https://mlsanity.com/paper/2204.09578/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [Time-Reversal Symmetric ODE Network](https://mlsanity.com/paper/2007.11362/) (2020)
- [A Novel Approach for Semiconductor Etching Process with Inductive Biases](https://mlsanity.com/paper/2104.02468/) (2021)

## Built on by (most cited first, 2 of 2)

- [PAC-Net: A Model Pruning Approach to Inductive Transfer Learning](https://mlsanity.com/paper/2206.05703/) (2022)
- [Revolutionizing TCAD Simulations with Universal Device Encoding and Graph Attention Networks](https://mlsanity.com/paper/2308.11624/) (2023)

## 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.