Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints

Abstract

We applied a Bayesian optimization (BO) to optimize the epitaxial growth process of Si thin films. The BO enables us to effectively explore the optimal film growth conditions considering several experimental parameters and their interactions. In this way we reduced the total number of experiments required in the optimization. The epitaxial growth rate was maximized while five quality parameters were maintained within an acceptable range. Additionally, we considered two practical issues: eliminating equipment errors and the time cost of the quality parameter evaluation. To overcome these issues, we adaptively conducted BO with different constraints for different situations. As a result of these optimizations, the crystal growth rate was increased to be approximately twice as high as that under standard conditions, while satisfying the five quality parameter conditions.