{"ID":2898195,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04153","arxiv_id":"2507.04153","title":"Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask","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.","short_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 expen...","url_abs":"https://arxiv.org/abs/2507.04153","url_pdf":"https://arxiv.org/pdf/2507.04153v1","authors":"[\"Vasiliy A. Es'kin\",\"Egor V. Ivanov\"]","published":"2025-07-05T20:21:31Z","proceeding":"math.NA","tasks":"[\"math.NA\",\"cs.AI\",\"cs.LG\",\"physics.comp-ph\",\"physics.optics\"]","methods":"[]","has_code":false}