Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control

Abstract

Substrate oxidation is inevitable when exposed to ambient atmosphere during semiconductor manufacturing, which is detrimental to the fabrication of state-of-the-art devices. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for random substrates poses a multidimensional challenge and is sometimes controversial. Due to variations in substrates and growth processes, the determination of the deoxidation condition heavily relies on the individual's expertise, yielding inconsistent results. This study employs a machine learning model that integrates interpolation and vision transformer (Interpolation-ViT) techniques. The model utilizes reflection high-energy electron diffraction videos as input to predict the status of the substrate, enabling automated deoxidation within a controlled architecture for various substrates. Furthermore, we highlight the potential of models trained on data from specific MBE equipment to achieve high-accuracy deployment on different pieces of equipment. In contrast to traditional methods, our approach holds exceptional value, as it standardizes deoxidation temperatures across diverse equipment and substrates. This significantly advances the standardization of the semiconductor process. The concepts and methods presented are expected to revolutionize semiconductor manufacturing processes in the optoelectronic and microelectronic industries.