Deep Learning for Semiconductor Materials and Devices Design

Abstract

This paper provides two examples of use cases of deep learning (DL) in the fabrication and design of modern semiconductor devices: modeling of plasma dry-etching process and transistor performance. The use cases demonstrate an improved version of DL model in terms of model accuracy and prediction time by incorporating scientific knowledge into DL.