Two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising materials for electronics, energy storage, and solar energy systems. Chemical vapor deposition (CVD) offers a scalable bottom-up route for synthesizing high-quality TMD films; however, optimization of operating conditions is challenging due to strongly coupled mass, momentum, and heat transfer phenomena. To address these complexities, a detailed CFD model incorporating transport equations and reaction kinetics is developed and validated by using published experimental data. A systematic design of experiments is then applied to generate a data set linking the operating temperature, pressure, and reactant flow rates to key film characteristics, including the deposition rate and uniformity. Surrogate machine learning models have been developed to reduce the computational cost associated with CFD simulations. Among several algorithms evaluated, Extreme Gradient Boosting (XGBoost) provides the highest predictive accuracy for MoS2 film growth behavior. Model interpretability is further enhanced using SHapley Additive exPlanations (SHAP), which reveal how reactor variables interact and contribute to deposition outcomes. Finally, the XGBoost model is integrated with the nondominated sorting genetic algorithm II (NSGA-II) to simultaneously maximize film thickness and uniformity in a cold-wall horizontal CVD reactor. The resulting optimal operating windows are visualized through parallel coordinate plots, providing practical guidelines for improving MoS2 film quality and enhancing CVD process efficiency.
Deivendran et al. (Tue,) studied this question.