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

TL;DR

This paper presents SemiEpi, a physics-informed machine learning platform for autonomous, on-demand growth of semiconductor heterostructures via molecular beam epitaxy, improving efficiency and precision in device fabrication.

Contribution

Introduction of SemiEpi, a self-driving, ML-guided system for heterostructure growth that reduces reliance on expert knowledge and trial-and-error methods.

Findings

Achieved high-density InAs quantum dots with targeted emission wavelength.

Increased photoluminescence intensity by 1.6 times.

Reduced FWHM to 29.13 meV using in-situ feedback control.

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.