@inproceedings{7501440c11964b38a38a56e5c783692b,
title = "Mask 3D model based on complex-valued convolution neural network for EUV lithography",
abstract = "The oblique incidence of extreme ultraviolet (EUV) light rays and the high aspect ratio of absorber layer on reflective mask induce pronounced mask three-dimensional (M3D) effects that significantly influence the imaging performance of EUV lithography. This paper proposes a fast learning-based M3D model dubbed complex-valued U-Net (CVU-Net) for EUV lithography simulation. The diffraction near-field (DNF) of EUV mask is calculated based on a set of complex-valued diffraction matrices, and each diffraction matrix can be rapidly synthesized using a well-trained CVU-Net. The network parameters are trained in a supervised manner. We set up a DNF dataset to train and test the proposed model. The comparison between the proposed method and some other M3D simulation methods is provided and discussed.",
keywords = "CVU-Net, EUV lithography, M3D model, diffraction near-field",
author = "Chengzhen Yu and Xu Ma and Junbi Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th International Workshop on Advanced Patterning Solutions, IWAPS 2022 ; Conference date: 21-10-2022 Through 22-10-2022",
year = "2022",
doi = "10.1109/IWAPS57146.2022.9972277",
language = "English",
series = "IWAPS 2022 - 2022 6th International Workshop on Advanced Patterning Solutions",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Yayi Wei and Tianchun Ye",
booktitle = "IWAPS 2022 - 2022 6th International Workshop on Advanced Patterning Solutions",
address = "United States",
}