Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916828964454400 |
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| author | Es'kin, Vasiliy A. Ivanov, Egor V. |
| author_facet | Es'kin, Vasiliy A. Ivanov, Egor V. |
| contents | Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from a mask are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, which is based on a waveguide method with its most computationally expensive part replaced by a neural network. Numerical experiments on realistic 2D and 3D masks show that the WGNO achieves state-of-the-art accuracy and inference time, providing a highly efficient solution for accelerating the design workflows of lithography masks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask Es'kin, Vasiliy A. Ivanov, Egor V. Numerical Analysis Artificial Intelligence Machine Learning Computational Physics Optics Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from a mask are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, which is based on a waveguide method with its most computationally expensive part replaced by a neural network. Numerical experiments on realistic 2D and 3D masks show that the WGNO achieves state-of-the-art accuracy and inference time, providing a highly efficient solution for accelerating the design workflows of lithography masks. |
| title | Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask |
| topic | Numerical Analysis Artificial Intelligence Machine Learning Computational Physics Optics |
| url | https://arxiv.org/abs/2507.04153 |