Feature Importance and Growth Rate Prediction in SiC PVT Processes through Advanced Machine Learning Models

Silicon carbide is a key wide-bandgap semiconductor material for next-generation power electronics, yet the Physical Vapor Transport (PVT) method used for bulk crystal growth remains constrained by complex thermal-chemical interactions and low growth rates. This study develops a data-driven framework to overcome these limitations by integrating a curated dataset of available experimental observations extracted from literature with machine learning (ML) models. The dataset includes seed temperature and equilibrium partial pressures of , and , with growth rate as the target. Feature scaling, normalization, and stratified sampling were applied to prepare training and test sets. Three modeling approaches—regularized linear regression, artificial neural networks, and k-nearest neighbors-were compared, with Gradient Boosting incorporated as a benchmark for advanced non-linear prediction. Results show that Gradient Boosting achieved the highest predictive accuracy ( ), outperforming and ). Feature selection revealed that gas-phase species, particularly exert slightly greater influence than temperature, emphasizing the coupled role of vapor chemistry and thermal fields. The findings demonstrate that machine learning provides a powerful alternative to traditional physics-based models, enabling more reliable prediction of SiC PVT growth rates and offering guidance for optimizing process design and control.