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Feature Adaptive Sampling for Scanning Electron Microscopy
A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-po...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858653/ https://www.ncbi.nlm.nih.gov/pubmed/27150131 http://dx.doi.org/10.1038/srep25350 |
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author | Dahmen, Tim Engstler, Michael Pauly, Christoph Trampert, Patrick de Jonge, Niels Mücklich, Frank Slusallek, Philipp |
author_facet | Dahmen, Tim Engstler, Michael Pauly, Christoph Trampert, Patrick de Jonge, Niels Mücklich, Frank Slusallek, Philipp |
author_sort | Dahmen, Tim |
collection | PubMed |
description | A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-posteriori image processing techniques were applied to remove the resulting noise. The technique was realized by scanning the sample twice. The first, quick scan used small pixel-dwell times to generate a first, noisy image using a low electron dose. This image was analyzed automatically, and a software algorithm generated a sparse pattern of regions of the image that require additional sampling. A second scan generated a sparse image of only these regions, but using a highly increased electron dose. By applying a selective low-pass filter and combining both datasets, a single image was generated. The resulting image exhibited a factor of ≈3 better SNR than an image acquired with uniform sampling on a Cartesian grid and the same total acquisition time. This result implies that the required electron dose (or acquisition time) for the adaptive scanning method is a factor of ten lower than for uniform scanning. |
format | Online Article Text |
id | pubmed-4858653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48586532016-05-19 Feature Adaptive Sampling for Scanning Electron Microscopy Dahmen, Tim Engstler, Michael Pauly, Christoph Trampert, Patrick de Jonge, Niels Mücklich, Frank Slusallek, Philipp Sci Rep Article A new method for the image acquisition in scanning electron microscopy (SEM) was introduced. The method used adaptively increased pixel-dwell times to improve the signal-to-noise ratio (SNR) in areas of high detail. In areas of low detail, the electron dose was reduced on a per pixel basis, and a-posteriori image processing techniques were applied to remove the resulting noise. The technique was realized by scanning the sample twice. The first, quick scan used small pixel-dwell times to generate a first, noisy image using a low electron dose. This image was analyzed automatically, and a software algorithm generated a sparse pattern of regions of the image that require additional sampling. A second scan generated a sparse image of only these regions, but using a highly increased electron dose. By applying a selective low-pass filter and combining both datasets, a single image was generated. The resulting image exhibited a factor of ≈3 better SNR than an image acquired with uniform sampling on a Cartesian grid and the same total acquisition time. This result implies that the required electron dose (or acquisition time) for the adaptive scanning method is a factor of ten lower than for uniform scanning. Nature Publishing Group 2016-05-06 /pmc/articles/PMC4858653/ /pubmed/27150131 http://dx.doi.org/10.1038/srep25350 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Dahmen, Tim Engstler, Michael Pauly, Christoph Trampert, Patrick de Jonge, Niels Mücklich, Frank Slusallek, Philipp Feature Adaptive Sampling for Scanning Electron Microscopy |
title | Feature Adaptive Sampling for Scanning Electron Microscopy |
title_full | Feature Adaptive Sampling for Scanning Electron Microscopy |
title_fullStr | Feature Adaptive Sampling for Scanning Electron Microscopy |
title_full_unstemmed | Feature Adaptive Sampling for Scanning Electron Microscopy |
title_short | Feature Adaptive Sampling for Scanning Electron Microscopy |
title_sort | feature adaptive sampling for scanning electron microscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858653/ https://www.ncbi.nlm.nih.gov/pubmed/27150131 http://dx.doi.org/10.1038/srep25350 |
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