Pioneering Excellence in Semiconductor Manufacturing via XGBoost with BAT Optimization for Cutting-Edge Predictive Maintenance

This study aims to revolutionize predictive maintenance in semiconductor manufacturing by using the SECOM dataset. The main objective is to create a precise and flexible predictive model by combining the XGBoost algorithm with BAT optimization techniques. The methodology involves intricate data preprocessing, incorporating data cleaning, handling multicollinearity, and using SMOTE-EEN to address the imbalanced SECOM dataset. The model is built using the robust XGBoost algorithm, and hyperparameter tuning is performed using BAT optimization techniques. The research results show great potential, with the model achieving a high accuracy of 0.99 and a strong F1 score of 0.95. The results confirm the model's effectiveness in predicting equipment failures in semiconductor manufacturing processes. The combination of XGBoost and BAT optimization techniques highlights the robustness and flexibility in dealing with the complex nature of the SECOM dataset. This study represents a significant advancement in predictive maintenance for semiconductor manufacturing. The integrated model provides a cutting-edge fault prediction solution, minimizing downtime and improving production efficiency.