# A Novel Approach for Semiconductor Etching Process with Inductive Biases

arXiv: https://arxiv.org/abs/2104.02468 · HTML: https://mlsanity.com/paper/2104.02468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)

## Built on by (most cited first, 3 of 3)

- [PAC-Net: A Model Pruning Approach to Inductive Transfer Learning](https://mlsanity.com/paper/2206.05703/) (2022)
- [Restructuring TCAD System: Teaching Traditional TCAD New Tricks](https://mlsanity.com/paper/2204.09578/) (2022)
- [Few-Shot Test-Time Optimization Without Retraining for Semiconductor Recipe Generation and Beyond](https://mlsanity.com/paper/2505.16060/) (2025)

## Abstract (arXiv)

The etching process is one of the most important processes in semiconductor manufacturing. We have introduced the state-of-the-art deep learning model to predict the etching profiles. However, the significant problems violating physics have been found through various techniques such as explainable artificial intelligence and representation of prediction uncertainty. To address this problem, this paper presents a novel approach to apply the inductive biases for etching process. We demonstrate that our approach fits the measurement faster than physical simulator while following the physical behavior. Our approach would bring a new opportunity for better etching process with higher accuracy and lower cost.