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.
Highlights
Structural defects can vastly alter the performance of materials so that control of defect distribution and density is an important tool in engineering materials with novel functionalities
We report the development of a method that applies cycle analysis from geometric graph theory to the positions of atomic-column centers and is capable of detecting a wide range of defects in STEM images with no prior knowledge of the material
In order to provide a concrete example of defect detection, we demonstrate in Fig. 2 how the above described algorithm finds a vacancy for a 2D material with a hexagonal lattice like graphene (Fig. 2a)
Summary
Structural defects can vastly alter the performance of materials so that control of defect distribution and density is an important tool in engineering materials with novel functionalities. Over the last two decades, aberration-corrected scanning-transmission-electronmicroscopy (STEM) has become a quantitative structural tool capable of locating atomic columns with picometerlevel precision. Defects within atomic columns can be detected by examining deviations in the contrast, looking for deviations in the local atomic-scale structure [10, 11], overlaying an ideal atomic-scale structure on the image [12], and by using vector tracing [13]. These methods include measuring the distance between neighboring atoms in the structure and using statistics and modeling
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