A mask optimization method based on self-calibrated convolutions is proposed in this paper to reduce the imaging distortion caused by optical proximity effect(OPE). The network model was constructed by combining the inverse lithography technology(ILT), and the parameters of the network model were optimized by the dataset for training. The dataset includes the target pattern and the mask optimized by gradient descent method. The network model based on selfcalibrated convolutions can output an optimized mask according to the target pattern, and the optimized mask is passed through the lithography forward model to obtain the exposure pattern. By the simulation experiment, compared with the traditional gradient-based method, proposed method in this paper has high computational efficiency and small error.
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.
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