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.6546 | CrystEngComm, 2018, 20, 6546-6550This journal is

The prediction model of the result of computed fluid dynamics simulation in SiC solution growth was constructed on neural network using machine learning. Utilizing the prediction model, we can optimize quickly crystal growth conditions. In addition, the real-time visualization system was also made using the prediction model.

In order to design a solvent for high-purity SiC solution growth, the impurity incorporation and the carbon solubility of various solvent materials have been investigated. Among the transition metal elements, the impurity elements of Cr, Ti, V and Hf are more readily incorporate during the solution growth than the other transition metal elements. The thermodynamic calculation revealed that the Y-Si solvent has relatively large carbon solubility, which is comparable to the Cr-Si and Ti-Si solvents often used in the solution growth of bulk SiC crystals. From these results, the Y-Si solvent is expected to be a suitable solvent for the high-purity SiC solution growth. Furthermore, we have demonstrated that the Y-Si solvent can achieve lower incorporation of metal impurity in the grown crystal than the Cr-Si solvent maintaining the growth rate.