Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

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 () and Zhanguo Wang
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Chao Shen: Institute of Semiconductors, Chinese Academy of Sciences
Wenkang Zhan: Institute of Semiconductors, Chinese Academy of Sciences
Kaiyao Xin: University of Chinese Academy of Science
Manyang Li: Institute of Semiconductors, Chinese Academy of Sciences
Zhenyu Sun: Institute of Semiconductors, Chinese Academy of Sciences
Hui Cong: University of Chinese Academy of Science
Chi Xu: University of Chinese Academy of Science
Jian Tang: Yancheng Teachers University
Zhaofeng Wu: Xinjiang University
Bo Xu: Institute of Semiconductors, Chinese Academy of Sciences
Zhongming Wei: University of Chinese Academy of Science
Chunlai Xue: University of Chinese Academy of Science
Chao Zhao: Institute of Semiconductors, Chinese Academy of Sciences
Zhanguo Wang: Institute of Semiconductors, Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm−2 down to 3.8 × 108 cm−2 or up to 1.4 × 1011 cm−2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

Date: 2024
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DOI: 10.1038/s41467-024-47087-w

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