Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography

Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography

The multilayer defects of mask blanks in extreme ultraviolet (EUV) lithography may cause severe reflectivity deformation and phase shift. The profile information of a multilayer defect is the key factor for mask defect compensation or repair. This paper introduces an artificial neural network framew...

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Veröffentlicht in:Applied optics (2004) 2023-02, Vol.62 (5), p.1243-1252
Hauptverfasser: Zheng, Hang, Li, Sikun, Cheng, Wei, Yuan, Shuai, Wang, Xiangzhao
Format: Artikel
Sprache:eng
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Artificial neural networks
Blanks
Defects
Extreme ultraviolet radiation
Generative adversarial networks
Lithography
Multilayers
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container_issue 5
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container_title Applied optics (2004)
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creator Zheng, Hang
Li, Sikun
Cheng, Wei
Yuan, Shuai
Wang, Xiangzhao
description The multilayer defects of mask blanks in extreme ultraviolet (EUV) lithography may cause severe reflectivity deformation and phase shift. The profile information of a multilayer defect is the key factor for mask defect compensation or repair. This paper introduces an artificial neural network framework to reconstruct the profile parameters of multilayer defects in the EUV mask blanks. With the aerial images of the defective mask blanks obtained at different illumination angles and a series of generative adversarial networks, the method enables a way of multilayer defect characterization with high accuracy.
doi_str_mv 10.1364/AO.480356
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subjects Artificial neural networks
Blanks
Defects
Extreme ultraviolet radiation
Generative adversarial networks
Lithography
Multilayers
title Phase defect characterization using generative adversarial networks for extreme ultraviolet lithography
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