Pre-trained CNN-based TransUNet Model for Mixed-Type Defects in Wafer Maps

8ad13b1e-a3bc-4138-827c-8243a9386f9620230719065308291wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS2224-34021790-083210.37394/23209http://wseas.org/wseas/cms.action?id=40461920231920232010.37394/23209.2023.20https://wseas.com/journals/isa/2023.phpPre-trained CNN-based TransUNet Model for Mixed-Type Defects in Wafer MapsYoungjaeKimDepartment of Computer Science and Engineering, Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon, 16419, REPUBLIC OF KOREAJee-HyongLeeDepartment of Computer Science and Engineering, Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon, 16419, REPUBLIC OF KOREAJongpilJeongDepartment of Computer Science and Engineering, Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon, 16419, REPUBLIC OF KOREAClassifying the patterns of defects in semiconductors is critical to finding the root cause of production defects. Especially as the concentration density and design complexity of semiconductor wafers increase, so do the size and severity of defects. The increased likelihood of mixed defects makes finding them more complex than traditional wafer defect detection methods. Manually inspecting wafers for defects is costly, creating a need for automated, artificial intelligence (AI)-based computer vision approaches. Previous research on defect analysis has several limitations, including low accuracy. To analyze mixed-type defects, existing research requires a separate model to be trained for each defect type, which is not scalable. In this paper, we propose a model for segmenting mixed defects by applying a pre-trained CNN-based TransUNet using N-pair contrastive loss. The proposed method allows you to extract an enhanced feature by repressing extraneous features and concentrating attention on the defects you want to discover. We evaluated the model on the Mixed-WM38 dataset with 38,015 images. 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