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; Zhanguo Wang

arXiv · 2306.12898

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·Zhanguo Wang

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Abstract

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applications strongly rely on the density and quality of these dots, which has motivated studies of the growth process control to realize high-quality epi-wafers and devices. 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 developed 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 demonstrated that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5E10 cm-2 down to 3.8E8 cm-2 or up to 1.4E11 cm-2. Compared to traditional methods, our approach, with in situ tuning capabilities and excellent reliability, can dramatically expedite the material optimization process and improve the reproducibility of MBE, constituting significant progress for thin film growth techniques. 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.

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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. 2023-06-22. Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots. https://arxiv.org/abs/2306.12898

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