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PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography

Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including mi...

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Autores principales: Jeong, Jaewoo, Kim, Taeyeong, Lee, Bong Jae, Lee, Jungchul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9065006/
https://www.ncbi.nlm.nih.gov/pubmed/35504973
http://dx.doi.org/10.1038/s41598-022-11185-w
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author Jeong, Jaewoo
Kim, Taeyeong
Lee, Bong Jae
Lee, Jungchul
author_facet Jeong, Jaewoo
Kim, Taeyeong
Lee, Bong Jae
Lee, Jungchul
author_sort Jeong, Jaewoo
collection PubMed
description Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures. Then, from this database, the morphology of buried sub-surface structure is determined only using surface topography. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3-D sub-surface analysis, the proposed methodology benefits from improved throughput compared to current inspection methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.
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spelling pubmed-90650062022-05-04 PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography Jeong, Jaewoo Kim, Taeyeong Lee, Bong Jae Lee, Jungchul Sci Rep Article Empty space in germanium (ESG) or germanium-on-nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures. Then, from this database, the morphology of buried sub-surface structure is determined only using surface topography. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3-D sub-surface analysis, the proposed methodology benefits from improved throughput compared to current inspection methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes. Nature Publishing Group UK 2022-05-03 /pmc/articles/PMC9065006/ /pubmed/35504973 http://dx.doi.org/10.1038/s41598-022-11185-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jeong, Jaewoo
Kim, Taeyeong
Lee, Bong Jae
Lee, Jungchul
PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title_full PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title_fullStr PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title_full_unstemmed PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title_short PCA-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
title_sort pca-based sub-surface structure and defect analysis for germanium-on-nothing using nanoscale surface topography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9065006/
https://www.ncbi.nlm.nih.gov/pubmed/35504973
http://dx.doi.org/10.1038/s41598-022-11185-w
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