High-speed prediction of computational fluid dynamics simulation in crystal growth

• Published in: CrystEngComm Vol. 2; no. 41; pp. 6546 - 655
• Main Authors: Tsunooka, Yosuke, Kokubo, Nobuhiko, Hatasa, Goki, Harada, Shunta, Tagawa, Miho, Ujihara, Toru
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 2018
• Subjects: Computational fluid dynamics; Computer simulation; Crystal growth; Crystals; Fluid dynamics; Machine learning; Neural networks; Optimization; Performance enhancement; Rapid prototyping; Semiconductor crystals; Semiconductor devices; Simulation
• ISSN: 1466-8033; 1466-8033
• DOI: 10.1039/c8ce00977e

Accelerating the optimization of material processing is essential for rapid prototyping of advanced materials to achieve practical applications. High-quality and large-diameter semiconductor crystals improve the performance, reliability and cost efficiency of semiconductor devices. However, much time is required to optimize the growth conditions and obtain a superior semiconductor crystal. Here, we demonstrate a rapid prediction of the results of computational fluid dynamics (CFD) simulations for SiC solution growth using a neural network for optimization of the growth conditions. The prediction speed was 10 7 times faster than that of a single CFD simulation. The combination of the CFD simulation and machine learning thus makes it possible to determine optimized parameters for high-quality and large-diameter crystals. Such a simulation is therefore expected to become the technology employed for the design and control of crystal growth processes. The method proposed in this study will also be useful for simulations of other processes. The combination of the CFD simulation and machine learning thus makes it possible to determine optimized parameters for high-quality and large-diameter crystals.