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- W4385408031 abstract "Extreme ultra-violet (EUV) lithography photomask defects are a common problem in the lithography printing process, which has a serious impact on the lithography printing process. Therefore, it is necessary to detect and quickly locate the defect. Many researchers have used image processing and machine learning methods to quickly identify defects in EUV photomasks and subsequently repair them. This paper proposes a detection method based on neural network image segmentation, and we introduce an improved U-Net to predict photomask defects. Our experiments show that the network model has better accuracy. In the process of identifying the defect image, it is in good agreement with the ground truth." @default.
- W4385408031 created "2023-08-01" @default.
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- W4385408031 date "2023-08-01" @default.
- W4385408031 modified "2023-09-27" @default.
- W4385408031 title "EUV photomask defect detection based on image segmentation" @default.
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- W4385408031 doi "https://doi.org/10.1117/12.2689144" @default.
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