Research on Quality Anomaly Detection and Early Warning of Semiconductor Chip Manufacturing Process Based on Machine Learning

Semiconductor chip manufacturing involves complex processes, high precision requirements, and multiple quality influencing factors. Traditional quality anomaly detection relies on manual experience and offline sampling, which suffers from latency and risk of missed inspections. This paper focuses on the application of machine learning in semiconductor manufacturing quality control, analyzing the limitations of current detection methods in complex data processing and real-time responsiveness. A machine learning-based quality anomaly detection and early warning framework is constructed, covering data preprocessing, feature engineering, model training, and early warning mechanism design. By comparing the performance of neural networks, random forests, SVM, and other algorithms on actual manufacturing data, this study validates the advantages of the proposed model in detection accuracy and early warning lead time. The research provides theoretical and technical references for enhancing the stability of semiconductor manufacturing processes and reducing defect rates.