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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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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. |
format | Online Article Text |
id | pubmed-9893472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
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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