Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask

Vasiliy A. Es'kin, Egor V. Ivanov
7/5/2025
math.NAcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{eskin2025physicsinformed,
  title  = {Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask},
  author = {Vasiliy A. Es'kin and Egor V. Ivanov},
  year   = {2025},
  eprint = {2507.04153},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.04153v1}
}

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