Optimization of carbon transport and growth rates in top-seeded solution growth of Al-doped SiC

Abstract

The top-seeded solution growth (TSSG) method is an emerging technique for the production of silicon carbide (SiC). Due to its advantage of lower growth temperature compared to the physical vapor transport method, it holds significant potential in the preparation of 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 the seed crystal on the flow pattern and carbon transport. The results indicated that a meticulous determination of these growth parameters could enhance both carbon transport and growth rate. Furthermore, abundant transient calculation results were utilized to train back-propagation (BP) neural networks to extract the correlation between growth parameters, growth rate, and Al concentration. The optimal parameters were ultimately obtained using the non-dominated sorting genetic algorithm (NSGA-II). The Al concentration calculated in the solution under the optimal growth conditions demonstrated that the evaporation of Al was sufficiently low to satisfy the p-type doping requirement. This study provides valuable insights for the future development of a TSSG system tailored for the rapid growth of Al-doped SiC.