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

TL;DR

This paper introduces a hybrid neural operator model called WGNO, combining waveguide methods with neural networks to efficiently simulate EUV wave diffraction, significantly improving accuracy and speed for lithography mask design.

Contribution

The paper presents a novel hybrid Waveguide Neural Operator that enhances simulation efficiency and accuracy for EUV wave diffraction problems.

Findings

WGNO achieves state-of-the-art accuracy

WGNO significantly reduces inference time

Effective for 2D and 3D mask simulations

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.