Fast diffraction model of an EUV mask based on asymmetric patch data fitting

Abstract

Calculating the diffraction near field (DNF) of a three-dimensional (3D) mask is a key problem in the extreme ultraviolet (EUV) lithography imaging modeling. This paper proposes a fast DNF model of an EUV mask based on the asymmetric patch data fitting method. Due to the asymmetric imaging characteristics of the EUV lithography system, a DNF library is built up including the training mask patches posed in different orientations and their rigorous DNF results. These training patches include some representative local mask features such as the convex corners, concave corners, and edge segments in four directions. Then, a convolution-based compact model is developed to rapidly simulate the DNFs of 3D masks, where the convolution kernels are inversely calculated to fit all of the training data. Finally, the proposed model is verified by simulation experiments. Compared to a state-of-the-art EUV mask model based on machine learning, the proposed method can further reduce the computation time by 60%–70% and roughly obtain the same simulation accuracy.