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

High Resolution Image Download MS PowerPoint Slide 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.