Reducing Wafer Scraps with AI: Detecting Hidden Damage in Vacuum Chambers

In semiconductor manufacturing, damage to certain parts of a vacuum chamber often goes undetected until the chamber is physically inspected or a defect map from processed wafers is obtained. This study proposes a machine learning (ML) based approach to analyze multivariate sensor data and detect hidden chamber damage in real time. By combining unsupervised and supervised learning models, the methodology demonstrated strong predictive capabilities in identifying key indicators of part damage. This approach enhances fault detection capabilities, reduces maintenance time, and prevents wafer losses. The findings highlight the potential of artificial intelligence (AI) and data-driven analytics in improving predictive maintenance and optimizing semiconductor manufacturing processes.