Application of a niching genetic algorithm to the optimization of a SiC crystal growth system

It was demonstrated that a niching genetic algorithm (NGA) could be efficient for the optimization of a SiC crystal growth system. And several design parameters of SiC crystal growth system could be optimized at the same time, and high diversity of population was maintained to obtain global optimization solution by NGA. Firstly, the NGA and thermal models were described and applied for SiC crystal growth by physical vapor transport (PVT) method, and the combination method of NGA and thermal models were presented. Then two cases were carried out to demonstrate the automatic optimization of SiC crystal growth system by NGA. One case was a single-objective optimization problem, in which the axial position of coils was optimized to improve the growth rate of crystal. The another was a multi-objective optimization problem, in which the thickness of substrate holder and input current were optimized for uniform temperature distribution along the growth surface for reducing the thermal stresses in growing crystal. Finally, all the optimization results were analyzed.