Composite Wafer Defect Recognition Framework Based on Multiview Dynamic Feature Enhancement With Class-Specific Classifier
Wenjun Luo, Huan Wang
Abstract
Wafer defect pattern recognition is important for the manufacturing of semiconductor products. By recognizing the type of defect, the engineer can optimize the process of semiconductor manufacturing. Due to the complexity of the manufacturing process, composite defect types can occur on a single wafer map. As a result, there are even more than 30 defect types in real-life, which greatly increases the difficulty of recognizing. To address this problem, we propose a composite wafer defect recognition framework (CWDR-Net) based on a multi-view dynamic feature enhancement (MVDFE) module with a class-specific classifier. This framework can selectively extract information from the defect pattern, and class-specifically recognize each basic defect type. Specifically, the proposed MVDFE-Module can view the feature from 3 different perspectives and dynamically enhance it accordingly. In addition, the proposed framework applies a class-specific classifier that uses an attention mechanism to recalibrate the feature for each type of basic defect respectively. A real dataset with 8 basic single-type defects, and 29 mixed-type composite defects is used to evaluate this framework. The results show that the proposed framework can effectively recognize composite defects and outperform other state-of-the-art methods.