Fast mask optimization based on self-calibrated convolutions

Yichen Zhao; Shuang Xu; Jinwei Pan

doi:10.1117/12.2689153

31 July 2023 Fast mask optimization based on self-calibrated convolutions

Yichen Zhao, Shuang Xu, Jinwei Pan

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127470E (2023) https://doi.org/10.1117/12.2689153
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

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.

Citation Download Citation

Yichen Zhao, Shuang Xu, and Jinwei Pan "Fast mask optimization based on self-calibrated convolutions", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127470E (31 July 2023); https://doi.org/10.1117/12.2689153

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KEYWORDS

Source mask optimization

Calibration

Convolution

Lithography

Education and training

Mathematical optimization

Imaging systems

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