Autonomous, Self-driving Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning

no code implementations • 7 Aug 2024 • Chao Shen, Wenkang Zhan, Hongyu Sun, Kaiyao Xin, Bo Xu, Zhanguo Wang, Chao Zhao

The semiconductor industry has prioritized automating repetitive tasks by closed-loop, autonomous experimentation which enables accelerated optimization of complex multi-step processes.

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

no code implementations • 4 Dec 2023 • Chao Shen, Wenkang Zhan, Jian Tang, Zhaofeng Wu, Bo Xu, Chao Zhao, Zhanguo Wang

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.

Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots

no code implementations • 22 Jun 2023 • Chao Shen, Wenkang Zhan, Kaiyao Xin, Manyang Li, Zhenyu Sun, Hui Cong, Chi Xu, Jian Tang, Zhaofeng Wu, Bo Xu, Zhongming Wei, Chunlai Xue, Chao Zhao, Zhanguo Wang

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources.