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

• URL: http://arxiv.org/abs/2507.04153v1
• Date: Sat, 05 Jul 2025 20:21:31 GMT
• Title: Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
• Abstract summary: 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.<n> Numerical experiments on realistic 2D and 3D masks show that the WGNO achieves the state-of-the-art accuracy and inference time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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