Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Image Processing Techniques and Applications · Integrated Circuits and Semiconductor Failure Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Smart Factory Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea
Funding
National Research Foundation of Korea
References (35)
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