Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps

Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using the proposed method, we can extract an improved feature map by suppressing irrelevant features and paying attention to the defect to be found. Experimental results show that the proposed model outperforms those in the existing studies.