Conclusion
In this study, we have proposed a Litho-AsymVnet framework to perform end-to-end superresolution lithography modeling. Our Litho-AsymVnet framework with an asymmetric autoencoder architecture takes in a lower resolution mask pattern image as input and produces a 6× higher resolution resist pattern image as output. To address the boundary pixel errors, we have proposed a “trimming” method and concentric binary cross-entropy loss function to achieve a good trade-off between prediction accuracy and runtime. The experimental results show that our proposed framework produces a high quality prediction of resist pattern compared with the prior work.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 62141414, 62350610271).
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Supporting information Appendix A. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
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Zhang, Q., Zhang, Y., Lu, W. et al. Litho-AsymVnet: super-resolution lithography modeling with an asymmetric V-net architecture. Sci. China Inf. Sci. 66, 229406 (2023). https://doi.org/10.1007/s11432-022-3755-y
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DOI: https://doi.org/10.1007/s11432-022-3755-y