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

Advanced Structural and Chemical Imaging - 2020
O. S. Ovchinnikov1,2, Andrew O’Hara2, Stephen Jesse1, Bethany M. Hudak3,4, Shize Yang3, Andrew R. Lupini3, Matthew F. Chisholm1, Wu Zhou3,5, Sergei V. Kalinin1, Albina Y. Borisevich3, Sokrates T. Pantelides3,6
1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, USA
2Department of Physics and Astronomy, Vanderbilt University, Nashville, USA
3Materials Sciences and Technology Division, Oak Ridge National Laboratory, Oak Ridge, USA
4Materials Science and Technology Division, US Naval Research Laboratory, Washington, DC, USA
5School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, China
6Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA

Tóm tắt

AbstractThe 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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Advanced Structural and Chemical Imaging

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