Optimal control of SiC crystal growth in the RF-TSSG system using reinforcement learning

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dc.contributor.authorWang, Lei
dc.contributor.authorSekimoto, Atsushi
dc.contributor.authorTakehara, Yuto
dc.contributor.authorOkano, Yasunori
dc.contributor.authorUjihara, Toru
dc.contributor.authorDost, Sadik
dc.date.accessioned2024-02-08T23:39:46Z
dc.date.available2024-02-08T23:39:46Z
dc.date.copyright2020en_US
dc.date.issued2020
dc.descriptionThe authors gratefully acknowledge the computational resources provided by the Research Institute for Information Technology at Kyushu University.en_US
dc.description.abstractWe 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.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThe research work was financially supported by Grant-in-Aid for Scientific Research (A) (JSPS KAKENHI Grant Number JP18H03839 and JP20H00320) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.en_US
dc.identifier.citationWang, 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/cryst10090791en_US
dc.identifier.urihttps://doi.org/10.3390/cryst10090791
dc.identifier.urihttp://hdl.handle.net/1828/15970
dc.language.isoenen_US
dc.publisherCrystalsen_US
dc.subjectSiC crystal growth
dc.subjectTSSG method
dc.subjectflow control
dc.subjectCrystal Growth Laboratory
dc.subject.departmentDepartment of Mechanical Engineering
dc.titleOptimal control of SiC crystal growth in the RF-TSSG system using reinforcement learningen_US
dc.typeArticleen_US

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