Fast diffraction model of lithography mask based on improved pixel-to-pixel generative adversarial network.

Abstract

Mask three-dimensional (3D) effect is a vital influence factor of imaging performance in the advanced extreme ultraviolet (EUV) lithography system. However, the rigorous 3D mask diffraction model is very time-consuming and brings a great computational burden. This paper develops a fast and accurate method to calculate the mask diffraction near-field (DNF) based on an improved pixel-to-pixel generative adversarial network, where the deformable convolution is introduced for fitting the crosstalk effect between mask feature edges. The long short-term memory model is added to the generator network to fuse and exchange information between the real parts and imaginary parts of DNF matrices. In addition, the simulation accuracy of DNF is enhanced by using the subpixel super-resolution method in the up-sampling step. The calculation accuracy is improved by more than 50% compared to the traditional network, and the calculational efficiency is improved by 128-folds compared to the rigorous electromagnetic field simulation method.