Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
Numerical Analysis
2025-07-08 v1 Artificial Intelligence
Machine Learning
Numerical Analysis
Computational Physics
Optics
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.
Keywords
Cite
@article{arxiv.2507.04153,
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},
journal= {arXiv preprint arXiv:2507.04153},
year = {2025}
}