Wang, LeiSekimoto, AtsushiTakehara, YutoOkano, YasunoriUjihara, ToruDost, Sadik2024-02-082024-02-0820202020Wang, L., Sekimoto, A., Takehara, Y., Okano, Y., Ujihara, T., & Dost, S. (2020). Optimal control of SIC crystal growth in the RF-TSSG system using reinforcement learning. Crystals, 10(9), 791. https://doi.org/10.3390/cryst10090791https://doi.org/10.3390/cryst10090791http://hdl.handle.net/1828/15970The authors gratefully acknowledge the computational resources provided by the Research Institute for Information Technology at Kyushu University.We have developed a reinforcement learning (RL) model to control the melt flow in the radio frequency (RF) top-seeded solution growth (TSSG) process for growing more uniform SiC crystals with a higher growth rate. In the study, the electromagnetic field (EM) strength is controlled by the RL model to weaken the influence of Marangoni convection. The RL model is trained through a two-dimensional (2D) numerical simulation of the TSSG process. As a result, the growth rate under the control of the RL model is improved significantly. The optimized RF-coil parameters based on the control strategy for the 2D melt flow are used in a three-dimensional (3D) numerical simulation for model validation, which predicts a higher and more uniform growth rate. It is shown that the present RL model can significantly reduce the development cost and offers a useful means of finding the optimal RF-coil parameters.enSiC crystal growthTSSG methodflow controlCrystal Growth LaboratoryArticleDepartment of Mechanical Engineering