On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning

• URL: http://arxiv.org/abs/2408.03508v4
• Date: Mon, 06 Oct 2025 02:26:28 GMT
• Title: On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning
• Authors: Chao Shen, Yuan Li, Wenkang Zhan, Shujie Pan, Fuxin Lin, Kaiyao Xin, Hui Cong, Chi Xu, Xiaotian Cheng, Ruixiang Liu, Zhibo Ni, Chaoyuan Jin, Bo Xu, Siming Chen, Zhongming Wei, Chunlai Xue, Zhanguo Wang, Chao Zhao,
• Abstract summary: SemiEpi is a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth.<n>We demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm.<n> Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth.
• Score: 17.8227548857444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters.

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