Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC

The macroscopic distribution of fluid flows, which affect the quality of final products for various kinds of materials, is often difficult to describe in mathematical formulae and hinders the implementation of empirical knowledge in scaling up. In the present study, the characteristics of the flow distribution in silicon carbide (SiC) solution growth are described by using the position of the saddle point and the solution growth conditions are optimized by computational fluid dynamics simulation, machine learning, and a genetic algorithm. As a result, the candidates of the optimal condition for the solution growth of 6‐in. SiC crystals are successfully obtained from the empirical knowledge gained from 3‐in. crystal growth, by adding the topological description to the objective function. The present design of the objective function using the topological description can possibly be applied to other crystal growth or materials processing problems and to overcome scale‐up difficulties, which can facilitate the rapid development of functional materials such as SiC wafers for power device applications. The application of the topological description of fluid flow to the design of the objective function for the optimization of the material process enables to implement the empirical knowledge gained from small‐size production in scaling up. The optimal conditions for the solution growth of 6‐in. silicon carbide wafers are successfully found from the empirical knowledge gained from 3‐in. wafer growth.