Bayesian optimization for a high- and uniform-crystal growth rate in the top-seeded solution growth process of silicon carbide under applied magnetic field and seed rotation

The Top-Seeded Solution Growth (TSSG) method is a promising technique for the production of high-quality SiC single crystal. To achieve a high- and uniform-growth rate in the TSSG process of SiC, the fluid flows developing in the growth solution (melt), due to the applied and induced electromagnetic fields, buoyancy, seed rotation, and free surface tension gradient, need to be controlled. Previous numerical analysis has shown that such complex flows in the TSSG melt can be controlled by the applications of a static magnetic field and seed rotation. However, the requirement of significant computational resources prevented us from carrying out the needed optimization for the process parameters involved. In order to resolve the computational demand issue, in this study, we utilized the Bayesian optimization algorithm for an efficient optimization of the associated control parameters of the TSSG process of SiC. It was shown that the Bayesian algorithm determines the optimal state at about roughly 1/4 of the computational cost of a conventional optimization, and accurately predicts the growth-rate evaluation function around the optimal state. The optimal state obtained by the present optimization process predicts a high- and uniform-growth rate in the TSSG system of SiC considered in this work.