Extreme Ultraviolet Multilayer Defect Profile Parameters Reconstruction via Transfer Learning with Fine-Tuned VGG-16.

Hala Mohammad, Jiawei Li, Bochao Li, Jamilu Tijjani Baraya, Sana Kone, Zhenlong Zhao, Xiaowei Song, Jingquan Lin

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Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Hala MohammadSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0003-3835-6328

Jiawei LiSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Bochao LiSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Jamilu Tijjani BarayaSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Sana KoneSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Zhenlong ZhaoSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Xiaowei SongSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Jingquan LinSchool of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Funding

National Natural Science Foundation of China (U22A2070, 62175018); Department of Science and Technology of Jilin Province (YDZJ202301ZYTS487, YDZJ202501ZYTS585); Education Department of the Jilin Province (JJKH20230793KJ); 111 Project of China (D17017); J 333045124508

6 · The paper itself

Abstract

Extracting defect profile parameters from measured defect images poses a significant challenge in extreme ultraviolet (EUV) multilayer defect metrologies, because these parameters are crucial for assessing defect printing behavior and determining appropriate repair strategies. This paper proposes to reconstruct defect profile parameters from reflected field intensity images of a phase defect assisted by transfer learning with fine-tuning. These images are generated through simulations using the rigorous finite-difference time-domain (FDTD) method. The VGG-16 pre-trained model, known for its robust feature extraction capability, is adopted and fine-tuned to map the intensity images to the defect profile parameters. The results demonstrate that the proposed approach accurately reconstructs multilayer defect profile parameters, thus providing important information for mask repair strategies.

Indexed as

EUV lithographyfine-tuningmultilayer defectstransfer learningVGG-16

Identifiers

PMID40428676

DOI10.3390/mi16050541

PMCPMC12113655

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