Optimization of Carbon Transport in Top-Seeded Solution Growth of Al-Doped Sic

Abstract

Top-seeded solution growth (TSSG) method is an emerging technique for producing silicon carbide (SiC). Due to its advantage of lower growth temperature compared to the physical vapor transport method, it holds great potential in preparing Al-doped SiC. In this study, a global numerical model calculating heat and mass transfer was established to investigate the impact of solution radius and height, coil position and rotational speed of seed crystal on the flow pattern and carbon transport. The results indicated that elaborate determination of these growth parameters could increase the carbon transport and growth rate. Furthermore, abundant transient calculation results were used to train back-propagation neural networks and the genetic algorithm models, to extract the relationship between growth parameters and growth rate. Ultimately, optimal crystal growth rate and parameters were obtained. Moreover, the calculated Al concentration in the solution under the optimal growth conditions showed that the evaporation of Al was sufficiently low to satisfy the p-type doping requirement. This work can guide for constructing a TSSG system for rapidly growing Al-doped SiC in the future.

Keywords: Silicon Carbide, Top-seeded solution growth, Numerical simulation, Machine Learning