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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9065006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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