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

arXiv: https://arxiv.org/abs/2507.04153 · HTML: https://mlsanity.com/paper/2507.04153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator](https://mlsanity.com/paper/2606.25753/) (2026)
- [Physics-Informed Neural Operator for Warm-Starting Background-Decomposed and Preconditioned PSFD: Enabling Scalable 3-D EUV Mask Simulation](https://mlsanity.com/paper/2607.25330/) (2026)
- [A Two-Mirror Faceted Projection System for EUV Lithography](https://mlsanity.com/paper/2609.11299/) (2026)

## Abstract (arXiv)

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.