Abstract

Near-field calculation for a three-dimensional (3D) mask is a fundamental task in extreme ultraviolet (EUV) lithography simulations. This paper develops a fast 3D mask near-field calculation method based on machine learning for EUV lithography. First, the training libraries of rigorous mask near fields are built based on a set of representative mask samples and reference source points. In the testing stage, the mask under consideration is first segmented into a set of non-overlapped patches. Then the local near field of each patch is calculated based on the non-parametric regression and data fusion techniques. Finally, the entire mask near field is synthesized based on the image stitching and data fitting methods. The proposed method is shown to achieve higher accuracy compared to the traditional domain decomposition method. In addition, the computational efficiency is improved up to an order of magnitude compared to the rigorous electromagnetic field simulator.

© 2020 Optical Society of America

Fast lithography aerial image calculation method based on machine learning

Xu Ma, Xuejiao Zhao, Zhiqiang Wang, Yanqiu Li, Shengjie Zhao, and Lu Zhang
Appl. Opt. 56(23) 6485-6495 (2017)

High-precision lithography thick-mask model based on a decomposition machine learning method

Ziqi Li, Lisong Dong, Xuyu Jing, Xu Ma, and Yayi Wei
Opt. Express 30(11) 17680-17697 (2022)

Fast rigorous mask model for extreme ultraviolet lithography

Zinan Zhang, Sikun Li, Xiangzhao Wang, and Wei Cheng
Appl. Opt. 59(24) 7376-7389 (2020)

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

Ziqi Li, Xuyu Jing, Lisong Dong, Xu Ma, and Yayi Wei
Appl. Opt. 62(25) 6561-6570 (2023)

Plasmonic lithography fast imaging model based on the decomposition machine learning method

Huwen Ding, Lihong Liu, Ziqi Li, Lisong Dong, Yayi Wei, and Tianchun Ye
Opt. Express 31(1) 192-210 (2023)