# Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator

arXiv: https://arxiv.org/abs/2606.25753 · HTML: https://mlsanity.com/paper/2606.25753/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask](https://mlsanity.com/paper/2507.04153/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Two-Mirror Faceted Projection System for EUV Lithography](https://mlsanity.com/paper/2609.11299/) (2026)

## Abstract (arXiv)

Gradient-based inverse lithography technology~(ILT) for extreme ultraviolet~(EUV) masks is presented. A novel framework treats the differentiable waveguide method and the recently proposed waveguide neural operator~(WGNO) as end-to-end physics engines, recovering the permittivity of the absorber of the mask through automatic differentiation of the full forward diffraction model. Numerical experiments on realistic 2D and 3D absorbers of the mask (TaBN, La, U) at λ=11.2~nm show that the considered ILT methods make it possible to obtain a mask structure that achieves the desired field on the wafer.