EUV multilayer defect characterization via cycle-consistent learning

Extreme ultraviolet (EUV) lithography mask defects may cause severe reflectivity deformation and phase shift in advanced nodes, especially like multilayer defects. Geometric parameter characterization is essential for mask defect compensation or repair. In this paper, we propose a machine learning f...

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Published inOptics express Vol. 28; no. 12; pp. 18493 - 18506
Main Authors Chen, Ying, Lin, Yibo, Chen, Rui, Dong, Lisong, Wu, Ruixuan, Gai, Tianyang, Ma, Le, Su, Yajuan, Wei, Yayi
Format Journal Article
LanguageEnglish
Published 08.06.2020
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Summary:Extreme ultraviolet (EUV) lithography mask defects may cause severe reflectivity deformation and phase shift in advanced nodes, especially like multilayer defects. Geometric parameter characterization is essential for mask defect compensation or repair. In this paper, we propose a machine learning framework to predict the geometric parameters of multilayer defects on EUV mask blanks. With the proposed inception modules and cycle-consistent learning techniques, the framework enables a novel way of defect characterization with high accuracy.
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ISSN:1094-4087
1094-4087
DOI:10.1364/OE.394590