A Novel Approach for Semiconductor Etching Process with Inductive Biases
This addresses the need for more accurate and cost-effective etching processes in semiconductor manufacturing, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of deep learning models for semiconductor etching profile prediction violating physics, by introducing a novel approach that applies inductive biases, resulting in faster fitting than physical simulators while following physical behavior.
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.