  The semiconductor industry has prioritized automating repetitive tasks by
closed-loop, autonomous experimentation which enables accelerated optimization
of complex multi-step processes. The emergence of machine learning (ML) has
ushered in automated process with minimal human intervention. In this work, we
develop SemiEpi, a self-driving automation platform capable of executing
molecular beam epitaxy (MBE) growth with multi-steps, continuous in-situ
monitoring, and on-the-fly feedback control. By integrating standard hardware,
homemade software, curve fitting, and multiple ML models, SemiEpi operates
autonomously, eliminating the need for extensive expertise in MBE processes to
achieve optimal outcomes. The platform actively learns from previous
experimental results, identifying favorable conditions and proposing new
experiments to achieve the desired results. We standardize and optimize growth
for InAs/GaAs quantum dots (QDs) heterostructures to showcase the power of
ML-guided multi-step growth. A temperature calibration was implemented to get
the initial growth condition, and fine control of the process was executed
using ML. Leveraging RHEED movies acquired during the growth, SemiEpi
successfully identified and optimized a novel route for multi-step
heterostructure growth. This work demonstrates the capabilities of closed-loop,
ML-guided systems in addressing challenges in multi-step growth for any device.
Our method is critical to achieve repeatable materials growth using
commercially scalable tools. Our strategy facilitates the development of a
hardware-independent process and enhancing process repeatability and stability,
even without exhaustive knowledge of growth parameters.
