Anomaly Detection Framework Based on Informed Machine Learning in Semiconductor Manufacturing Equipment

Abstract

While the utilization of data in manufacturing environments continues to expand, the integration of domain knowledge remains crucial for practical implementation. We have systematically structured a machine learning framework that incorporates domain knowledge, which is called Informed Machine Learning (IML). Then, we focus on its practical applications in fault detection systems for semiconductor lithography equipment, which plays a vital role in semiconductor manufacturing processes. This equipment, responsible for transferring circuit patterns onto silicon wafers, consists of high-precision optical systems and multiple units requiring precise control. Notably, the conventional method of fault detection cannot be performed due to the limited occurrence of failures in our problem, where it is difficult to obtain adequate labeled data beforehand. To tackle such a problem, we propose a practical fault detection framework that integrates expert knowledge correlating failure modes with equipment states, combined with sensor-based condition monitoring.