Development and evaluation of the automated experimental design system using evolutionary multi-objective optimization in CVD method

Abstract

The target of this study is the CVD process for the fabrication of semiconductor devices. In order to automate of the entire process for research and development of CVD, we developed and evaluated the system that automatically design the experiments for identifying reaction models. In our conventional system, the algorithms using CMA-ES were adopted. However, many dominated solutions were generated, and many candidates for experimental condition that were not effective on identifying reaction models were proposed. Therefore, in this study, the algorithms using SPEA2 were adopted to try to generate the Pareto solution group by avoiding the dominated solution group. In addition, the x-means method that automatically determines the number of clusters was introduced as a method for classifying the obtained solutions, aiming at further automation of the system.