Universal Deoxidation of Semiconductor Substrates Assisted by
  Machine-Learning and Real-Time-Feedback-Control
Chao Shen Wenkang Zhan Jian Tang Zhaofeng Wu Bo Xu Chao Zhao Zhanguo Wang
Thin film deposition is an essential step in the semiconductor process.
During preparation or loading, the substrate is exposed to the air unavoidably,
which has motivated studies of the process control to remove the surface oxide
before thin film deposition. Optimizing the deoxidation process in molecular
beam epitaxy (MBE) for a random substrate is a multidimensional challenge and
sometimes controversial. Due to variations in semiconductor materials and
growth processes, the determination of substrate deoxidation temperature is
highly dependent on the grower's expertise; the same substrate may yield
inconsistent results when evaluated by different growers. Here, we employ a
machine learning (ML) hybrid convolution and vision transformer (CNN-ViT)
model. This model utilizes reflection high-energy electron diffraction (RHEED)
video as input to determine the deoxidation status of the substrate as output,
enabling automated substrate deoxidation under a controlled architecture. This
also extends to the successful application of deoxidation processes on other
substrates. Furthermore, we showcase the potential of models trained on data
from a single MBE equipment to achieve high-accuracy deployment on other
equipment. In contrast to traditional methods, our approach holds exceptional
practical value. It standardizes deoxidation temperatures across various
equipment and substrate materials, advancing the standardization research
process in semiconductor preparation, a significant milestone in thin film
growth technology. The concepts and methods demonstrated in this work are
anticipated to revolutionize semiconductor manufacturing in optoelectronics and
microelectronics industries by applying them to diverse material growth
processes.