[22p-M206-1~16] 23.1 Joint Session N "Informatics"

▼ [22p-M206-6] Optimal control of growth interface shape through machine learning in the growth of InGaSb crystal under microgravity

〇(D)Rachid Ghritli¹, Yasunori Okano¹, Yuko Inatomi^2,3 (1.Osaka Univ., 2.JAXA, 3.SOKENDAI)

Keywords:Crystal Growth, Machine Learning, Reinforcement Learning

Growth of high quality of InGaSb crystals by Vertical Gradient Freezing under microgravity conditions was numerically simulated. Machine learning tools such as Bayesian Optimization and Reinforcement Learning were used to optimize the growth conditions. The study focuses on controlling the growth interface shape which directly affects the quality and homogeneity of the grown crystals. The system was subjected to a lower temperature gradient near the feed crystal and to crucible rotation with a rate ranging according to the obtained optimal strategy. Consequently, the interface deformation was considerably reduced, and a flatter growth interface could be maintained. The growth rate and solute concentration uniformity were also improved.