Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask

TL;DR

This paper introduces physics-informed neural networks and neural operators, including a novel hybrid Waveguide Neural Operator, to efficiently simulate EUV wave diffraction from lithography masks, achieving high accuracy and faster predictions.

Contribution

The paper presents a new hybrid Waveguide Neural Operator architecture that replaces computationally expensive components with neural networks, improving simulation speed and accuracy for EUV diffraction problems.

Findings

PINNs and neural operators achieve competitive accuracy with traditional solvers.

The hybrid WGNO reaches state-of-the-art performance in simulation tasks.

Neural operators demonstrate strong generalization to unseen parameters.

Abstract

Physics-informed neural networks (PINNs) and neural operators (NOs) for solving the problem of diffraction of Extreme Ultraviolet (EUV) electromagnetic waves from contemporary lithography masks are presented. A novel hybrid Waveguide Neural Operator (WGNO) is introduced, based on a waveguide method with its most computationally expensive components replaced by a neural network. To evaluate performance, the accuracy and inference time of PINNs and NOs are compared against modern numerical solvers for a series of problems with known exact solutions. The emphasis is placed on investigation of solution accuracy by considered artificial neural systems for 13.5 nm and 11.2 nm wavelengths. Numerical experiments on realistic 2D and 3D masks demonstrate that PINNs and neural operators achieve competitive accuracy and significantly reduced prediction times, with the proposed WGNO architecture reaching state-of-the-art performance. The presented neural operator has pronounced generalizing properties, meaning that for unseen problem parameters it delivers a solution accuracy close to that for parameters seen in the training dataset. These results provide a highly efficient solution for accelerating the design and optimization workflows of next-generation lithography masks.