Wafer

Semiconductor manufacturing is a highly complex process requiring precise control and monitoring to maintain product quality and yield. This research presents a comprehensive comparative analysis of three machine learning algorithms—Random Forest, Support Vector Machine (SVM), and XGBoost—for anomaly detection in semiconductor fabrication. Through extensive experimentation using a real-world wafer dataset, we demonstrate that XGBoost outperforms other models, achieving 97.1\% accuracy, 96.4\% precision, and 95.0\% recall. The study further explores implementation challenges, real-time deployment considerations, and future directions including explainable AI approaches. Our findings establish a practical framework for implementing machine learning-based anomaly detection in semiconductor manufacturing environments, contributing to enhanced process control and yield optimization.