SemiEpi: Self-driving, Closed-loop Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning

• URL: http://arxiv.org/abs/2408.03508v3
• Date: Sun, 05 Jan 2025 10:05:05 GMT
• Title: SemiEpi: Self-driving, Closed-loop Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning
• Authors: Chao Shen, Wenkang Zhan, Kaiyao Xin, Shujie Pan, Xiaotian Cheng, Ruixiang Liu, Zhe Feng, Chaoyuan Jin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Zhanguo Wang, Chao Zhao,
• Abstract summary: SemiEpi is a self-driving platform designed to execute molecular beam epitaxy (MBE) growth of semiconductor heterostructures.<n>SemiEpi identifies optimal initial conditions and proposes experiments for multi-step heterostructure growth.<n>We optimize the growth for InAs quantum dots (QDs) heterostructures to showcase the power of SemiEpi.
• Score: 19.232018561757545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The semiconductor industry has prioritized automating repetitive tasks through closed-loop, self-driving experimentation, accelerating the optimization of complex multi-step processes. The emergence of machine learning (ML) has ushered in self-driving processes with minimal human intervention. This work introduces SemiEpi, a self-driving platform designed to execute molecular beam epitaxy (MBE) growth of semiconductor heterostructures through multi-step processes, in-situ monitoring, and on-the-fly feedback control. By integrating standard reactor, parameter initialization, and multiple ML models, SemiEpi identifies optimal initial conditions and proposes experiments for multi-step heterostructure growth, eliminating the need for extensive expertise in MBE processes. SemiEpi initializes material growth parameters tailored to specific material characteristics, and fine-tuned control over the growth process is then achieved through ML optimization. We optimize the growth for InAs quantum dots (QDs) heterostructures to showcase the power of SemiEpi, achieving a QD density of 5E10/cm2, 1.6-fold increased photoluminescence (PL) intensity and reduced full width at half maximum (FWHM) of 29.13 meV. This work highlights the potential of closed-loop, ML-guided systems to address challenges in multi-step growth. Our method is critical to achieve repeatable materials growth using commercially scalable tools. Furthermore, our strategy facilitates developing a hardware-independent process and enhancing process repeatability and stability, even without exhaustive knowledge of growth parameters.

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