Mixed-Type Wafer Defect Pattern Recognition Framework Based on Multifaceted Dynamic Convolution

Abstract

Wafer Defect pattern recognition is an essential process in semiconductor manufacturing. This helps the manufacturers improve their fabrication process, reduce initial costs, and avoid further defects. However, mixed-type defects can be varied and complicated, which is difficult for traditional deep learning model to learn their features. To address this problem, we utilize dynamicity to allow the adaptive integration of information. Therefore, this paper proposed a mixed-type wafer defect pattern recognition framework based on multi-faceted dynamic convolution (MFD). This framework can effectively detect decisive features and integrate helpful information from defect patterns. Specifically, the proposed MFD firstly designs a 1-D auto-encoder to process the input, and extends it to a 3-D to obtain trans-pixel dynamic perception. In addition, we apply a channel-amalgamating mechanism to obtain the trans-channel dynamic perception and further synthesize the information across the channel. We appraised our model based on a dataset with 38 defect patterns generated from real industrial manufacturing. The results showed that the proposed framework excelled the vanilla CNN and state-of-the-art deep learning models. Further interpretable analysis based on visualization also indicated that this framework can adaptively procure the significant features from different defects.