A Novel Approach for Semiconductor Etching Process with Inductive Biases

TL;DR

This paper introduces a deep learning-based method for semiconductor etching that incorporates inductive biases to improve prediction accuracy, physical consistency, and computational efficiency, offering a promising alternative to traditional physical simulators.

Contribution

The paper presents a novel approach applying inductive biases in deep learning models to better capture physical behaviors in semiconductor etching processes.

Findings

Faster fitting to measurements than physical simulators

Maintains physical behavior in predictions

Potential for higher accuracy and lower cost

Abstract

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.