Detection of defects in atomic-resolution images of materials using cycle analysis

Oleg S. Ovchinnikov; Andrew O’Hara; Stephen Jesse; Bethany M. Hudak; Shi‐Ze Z. Yang; Andrew R. Lupini; Matthew F. Chisholm; Wu Zhou; Sergei V. Kalinin; Albina Y. Borisevich; Sokrates T. Pantelides

doi:10.1186/s40679-020-00070-x

Detection of defects in atomic-resolution images of materials using cycle analysis

Oleg S. Ovchinnikov, Andrew O’Hara, Stephen Jesse, Bethany M. Hudak, Shi‐Ze Z. Yang, Andrew R. Lupini, Matthew F. Chisholm, Wu Zhou, Sergei V. Kalinin, Albina Y. Borisevich, Sokrates T. Pantelides

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

Original language	English
Article number	3
Journal	Advanced Structural and Chemical Imaging
Volume	6
Issue number	1
DOIs	https://doi.org/10.1186/s40679-020-00070-x
State	Published - Dec 1 2020

Funding

Data analysis was supported by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division (OSO, SJ, SVK, AYB). Part of the data analysis was supported by US Department of Energy Grant DE-FG-02-09ER46554 and by the Vanderbilt McMinn Endowment (AO, STP). Electron microscopy was supported by the US Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division (BMH, SZY, ARL, MFC, WZ). Experiments were in part conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility.

Funders	Funder number
OSO
Vanderbilt McMinn Endowment
U.S. Department of Energy	DE-FG-02-09ER46554
Office of Science
Basic Energy Sciences
Division of Materials Sciences and Engineering

Keywords

2D materials
Atomic resolution
Automation
Defect detection
Defects
STEM
Surfaces

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10.1186/s40679-020-00070-x

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title = "Detection of defects in atomic-resolution images of materials using cycle analysis",

abstract = "The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material{\textquoteright}s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.",

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author = "Ovchinnikov, {Oleg S.} and Andrew O{\textquoteright}Hara and Stephen Jesse and Hudak, {Bethany M.} and Yang, {Shi‐Ze Z.} and Lupini, {Andrew R.} and Chisholm, {Matthew F.} and Wu Zhou and Kalinin, {Sergei V.} and Borisevich, {Albina Y.} and Pantelides, {Sokrates T.}",

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AU - O’Hara, Andrew

AU - Jesse, Stephen

AU - Hudak, Bethany M.

AU - Yang, Shi‐Ze Z.

AU - Lupini, Andrew R.

AU - Chisholm, Matthew F.

AU - Zhou, Wu

AU - Kalinin, Sergei V.

AU - Borisevich, Albina Y.

AU - Pantelides, Sokrates T.

PY - 2020/12/1

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N2 - The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

AB - The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

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Detection of defects in atomic-resolution images of materials using cycle analysis

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