FAULT DETECTION FOR THE CLASS IMBALANCE PROBLEM IN SEMICONDUCTOR MANUFACTURING PROCESSES
Abstract
In the semiconductor manufacturing process, fault detection which aims at constructing a decision tool to maintain high process yields is a major step of the process control. Unfortunately, the class imbalance in the modern semiconductor industry makes feature selection for fault detection quite challenging. However, the characteristic has usually been ignored in the open literatures. This paper analyzes the challenge and indicates some of the reasons are due to the dataset shift, the small samples and the class overlapping caused by the class imbalance. To cope with the problems, a new feature selection approach is proposed, which combines the global and local resampling and named ensemble manifold sensitive margin fisher analysis (EMSMFA). Our approach consists of three key components: (1) At the global level, the bagging-based ensemble model is used to overcome the overfitting caused by the data shift; (2) At the local level, the manifold-based oversampling named the weighted synthetic minority oversampling technique (WSMOTE) is proposed to solve the small samples problem in the minority class; (3) The sensitive margin fisher analysis (SMFA) is used to solve the challenge caused by the class overlapping. The proposed fault detection method is demonstrated through its application to the semiconductor wafer fabrication process. The experimental results confirm the EMSMFA improves the performance of fault detection.
This paper was recommended by Regional Editor Tongquan Wei.
