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

Abstract

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. . IntroductionFluid flow that arises in many materials processing techniques, such as crystal growth, casting, thin film fabrication, and polymer processing, is critical to the quality and characteristics of the final product and optimization of the system. [1] Recent advances in computational fluid dynamics (CFD) aid in the design of suitable conditions for materials processing. [2] Furthermore, the

Data-driven automated control algorithm for floating-zone crystal growth derived by reinforcement learning

“…The application of informatics has enabled us to realize efficient optimization, automation and advances in materials processing [1][2][3][4][5][6][7][8][9] . The design of conditions and environments for materials processing has been efficiently optimized using surrogate models built by neural networks or other machine learning algorithms 1,2,6,[10][11][12][13] . Bayesian optimization can successfully reduce the number of trials for the acquisition of favorable conditions for materials processing [14][15][16][17] .…”