Mixed-Type Wafer Defect Recognition With Multi-Scale Information Fusion Transformer

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Defect pattern recognition (DPR) of wafer maps can be essential as the accurate classification helps with the fabrication process improvement and thus avoiding further defects. During fabrication, various defect patterns may be mixed. In contrast to single-type DPR, mixed-type DPR can be much more complicated due to the varied spatial features, numerous types, uncertain number of defects, etc. To effectively recognize the mixed-type defects, we proposed a novel multi-scale information fusion transformer framework (MSF-Trans). Specifically, an MSF-Network is proposed to focus on the detailed features of wafer maps, which can also selectively enhance valuable information. Subsequently, the Transformer architecture is introduced, which used multi-head attention mechanism to encode the global context features of wafer maps, thereby modeling the internal relationship between wafer maps and defect patterns. MSF-Trans fully integrates the advantages of convolutional network and transformer in detailed feature learning and global feature learning. MSF-Trans is evaluated on a real dataset with 38 defect patterns. The results show that MSF-Trans has excellent defect recognition ability and is significantly better than the existing deep learning algorithms. Further interpretable analysis indicates that MSF-Trans can effectively recognize the defect pattern and enhance learning of valuable information, which facilitates the recognition of mixed-type defects.