Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenides with Sub-Picometer Precision

• DOI: 10.1021/acs.nanolett.0c00269
• Publication Date: 2020-04-03T20:10:46Z
• Authors: Chia-Hao Lee; Abid Khan; Di Luo; Tatiane P. Santos; Chuqiao Shi; Blanka E. Janicek; Sangmin Kang; Wenjuan Zhu; Nahil A. Sobh; André Schleife; Bryan K. Clark; Pinshane Y. Huang
• Keywords: Atomic units; Lattice (music)

Two-dimensional (2D) materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so far limited electron microscopy methods probe these atomic-scale fields. Here, we demonstrate approach single-atom defects sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning mine large data sets of aberration-corrected scanning transmission images locate and classify point defects. By combining hundreds nominally identical generate high signal-to-noise class averages which allow us measure spacings up 0.2 pm precision. Our reveal that Se vacancies introduce complex, oscillating WSe2-2xTe2x lattice correspond alternating rings expansion contraction. These results indicate potential impact computer vision for development high-precision beam-sensitive materials.