EUV photomask defect detection based on image segmentation

Xianhao Peng; Shuang Xu; Yichen Zhao

doi:10.1117/12.2689144

31 July 2023 EUV photomask defect detection based on image segmentation

Xianhao Peng, Shuang Xu, Yichen Zhao

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127470Z (2023) https://doi.org/10.1117/12.2689144
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

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.

Citation Download Citation

Xianhao Peng, Shuang Xu, and Yichen Zhao "EUV photomask defect detection based on image segmentation", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127470Z (31 July 2023); https://doi.org/10.1117/12.2689144

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KEYWORDS

Photomasks

Image segmentation

Extreme ultraviolet

Defect detection

Image processing

Neural networks

Feature fusion

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