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Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography

[Image: see text] The line edge roughness (LER) is one of the most critical indicators of photoresist imaging performance, and its measurement using a reliable method is of great significance for lithography. However, most studies only investigate photoresist resolution and sensitivity because LER m...

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Autores principales: Hu, Ziyu, Zhao, Rongbo, Wang, Xiaolin, Tao, Peipei, Wang, Qianqian, Wang, Yimeng, Xu, Hong, He, Xiangming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893472/
https://www.ncbi.nlm.nih.gov/pubmed/36743030
http://dx.doi.org/10.1021/acsomega.2c06769
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author Hu, Ziyu
Zhao, Rongbo
Wang, Xiaolin
Tao, Peipei
Wang, Qianqian
Wang, Yimeng
Xu, Hong
He, Xiangming
author_facet Hu, Ziyu
Zhao, Rongbo
Wang, Xiaolin
Tao, Peipei
Wang, Qianqian
Wang, Yimeng
Xu, Hong
He, Xiangming
author_sort Hu, Ziyu
collection PubMed
description [Image: see text] The line edge roughness (LER) is one of the most critical indicators of photoresist imaging performance, and its measurement using a reliable method is of great significance for lithography. However, most studies only investigate photoresist resolution and sensitivity because LER measurements require an expensive and not widely available critical dimension scanning electron microscopy (SEM) technology; thus, the imaging performance of photoresist has not been adequately evaluated. Here, we report an image processing software developed for offline calculation of LER that can analyze lithographic patterns with resolutions up to ∼15 nm. This software can effectively process all graphic files obtained from commonly used SEM machines by utilizing the adjustable double threshold. To realize the effective detection of high-resolution patterns in advanced lithography, we used SEM images generated from extreme ultraviolet and electron beam lithography to develop and validate the software’s graphic recognition algorithm. This image processing software can process typical SEM images and produce reliable LER in an efficient and user-friendly manner, constituting a powerful tool for promoting the development of high-performance photoresist materials.
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spelling pubmed-98934722023-02-03 Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography Hu, Ziyu Zhao, Rongbo Wang, Xiaolin Tao, Peipei Wang, Qianqian Wang, Yimeng Xu, Hong He, Xiangming ACS Omega [Image: see text] The line edge roughness (LER) is one of the most critical indicators of photoresist imaging performance, and its measurement using a reliable method is of great significance for lithography. However, most studies only investigate photoresist resolution and sensitivity because LER measurements require an expensive and not widely available critical dimension scanning electron microscopy (SEM) technology; thus, the imaging performance of photoresist has not been adequately evaluated. Here, we report an image processing software developed for offline calculation of LER that can analyze lithographic patterns with resolutions up to ∼15 nm. This software can effectively process all graphic files obtained from commonly used SEM machines by utilizing the adjustable double threshold. To realize the effective detection of high-resolution patterns in advanced lithography, we used SEM images generated from extreme ultraviolet and electron beam lithography to develop and validate the software’s graphic recognition algorithm. This image processing software can process typical SEM images and produce reliable LER in an efficient and user-friendly manner, constituting a powerful tool for promoting the development of high-performance photoresist materials. American Chemical Society 2023-01-18 /pmc/articles/PMC9893472/ /pubmed/36743030 http://dx.doi.org/10.1021/acsomega.2c06769 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Hu, Ziyu
Zhao, Rongbo
Wang, Xiaolin
Tao, Peipei
Wang, Qianqian
Wang, Yimeng
Xu, Hong
He, Xiangming
Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title_full Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title_fullStr Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title_full_unstemmed Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title_short Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography
title_sort canny algorithm enabling precise offline line edge roughness acquisition in high-resolution lithography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893472/
https://www.ncbi.nlm.nih.gov/pubmed/36743030
http://dx.doi.org/10.1021/acsomega.2c06769
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