Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
This provides an efficient solution for accelerating design workflows in lithography, a domain-specific incremental improvement.
The paper tackles the problem of simulating Extreme Ultraviolet electromagnetic wave diffraction from lithography masks by introducing a hybrid Waveguide Neural Operator, which achieves state-of-the-art accuracy and inference time in numerical experiments on realistic 2D and 3D masks.
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.