Explainable Analysis and Optimization of the Thermal Field in Gallium Oxide Single Crystal Growth Based on Machine Learning

The edge-defined film-fed growth (EFG) method, as the core technology for growing high-quality gallium oxide (β-Ga2O3) single crystals, constitutes a complex system engineering. The precise control of thermal field distribution during crystal growth has emerged as one of the critical factors for enhancing crystal quality. Machine learning (ML) techniques have demonstrated tremendous potential in optimizing the thermal field design, particularly in predicting the influence of equipment dimensions on thermal field distribution. Centered on the application of ML in the thermal field design of β-Ga2O3, this paper proposes a prediction model based on equipment dimensions. A dataset containing 5000 sets of geometric parameters of equipment was constructed. A predictive model was established based on the machine learning Categorical Boosting (CatBoost) model, and explainable ML models were employed to analyze the relationship between equipment dimensions and thermal field distribution. Shapley additive explanation values were utilized to quantify the impact of inputs on outputs, thereby identifying the key indicators influencing the thermal field design in the EFG method. The optimal geometric configuration was obtained through multiobjective optimization, facilitating the growth of higher-quality β-Ga2O3 single crystals. Finally, the key indicator factors affecting the temperature gradient at the solid–liquid interface were verified through experiments.