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

被引:12
|
作者
Ovchinnikov, Oleg S. [1 ,2 ]
O'Hara, Andrew [1 ]
Jesse, Stephen [2 ]
Hudak, Bethany M. [3 ,4 ]
Yang, Shi-Ze [3 ]
Lupini, Andrew R. [3 ]
Chisholm, Matthew F. [2 ]
Zhou, Wu [3 ,5 ]
Kalinin, Sergei, V [2 ]
Borisevich, Albina Y. [3 ]
Pantelides, Sokrates T. [1 ,3 ,6 ]
机构
[1] Vanderbilt Univ, Dept Phys & Astron, Nashville, TN 37235 USA
[2] Oak Ridge Natl Lab, Ctr Nanophase Mat Sci, Oak Ridge, TN USA
[3] Oak Ridge Natl Lab, Mat Sci & Technol Div, Oak Ridge, TN USA
[4] US Naval Res Lab, Mat Sci & Technol Div, Washington, DC USA
[5] Univ Chinese Acad Sci, Sch Phys Sci, CAS Key Lab Vacuum Phys, Beijing, Peoples R China
[6] Vanderbilt Univ, Dept Elect Engn & Comp Sci, Nashville, TN 37235 USA
来源
ADVANCED STRUCTURAL AND CHEMICAL IMAGING | 2020年 / 6卷 / 01期
关键词
Defects; Defect detection; Automation; Atomic resolution; 2D materials; Surfaces; STEM; ELECTRON;
D O I
10.1186/s40679-020-00070-x
中图分类号
TH742 [显微镜];
学科分类号
摘要
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.
引用
收藏
页数:9
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