Investigating the Growth of GaSb Single Crystals through Optimized LEC Method Utilizing Finite Element Simulation and Machine Learning Techniques

In this study, CGsim and liquid-encapsulated Czochralski (LEC) growth experiments were employed to handle the challenges associated with growing large-sized compound semiconductor single crystals. CGsim, a simulation software integrating the finite element method with machine learning (ML) techniques, was utilized to optimize the heat flux and crystallization front morphology at the solid-liquid interface during GaSb crystal growth. ML validation, performed across various crucible rotation speeds and crystal position (CP) configurations, enabled the optimization of the moving front shape at the solid-liquid interface, reducing the protrusion angle to 0.086°. The crystal quality of GaSb single crystal slices was evaluated through X-ray double crystal rocking curves and optical microscopy. The results indicate that the optimized growth parameters reduced the dislocation density in 6-in. GaSb single crystals from 1039 to 369 cm^-2 and narrowed the X-ray rocking curve full width at half-maximum (fwhm) from 29 to 28.5 arcsec. The steady-state/unsteady-state simulations conducted in CGsim, combined with ML-optimized growth parameters, significantly lowered the likelihood of defect formation, involving dislocation clustering, vacancy defects, twins, undercooling striations, and small-angle grain boundaries.