EUROPEAN CONGRESS AND EXHIBITION ON ADVANCED MATERIALS AND PROCESSES - EUROMAT 2021

The controlled introduction of impurities or defects within the lattice of 2D materials is a promising strategy for tailoring their properties. In this context, advanced microscopy techniques such as aberration-corrected scanning transmission electron microscopy (STEM) are able to characterize low-dimensional structures by resolving every atom, enabling the assignment of atomic bonding and elemental composition based on the image contrast. However, even if the characterization of the introduced disorder would be notably more accurate than with other well-established methods, the manual operation of these instruments and the resulting time-consuming acquisition of images made, until now, an approach based on atomic resolution images not suitable for performing an in-depth statistical analysis of the introduced disorder. Furthermore, to ensure large-scale atomic resolution, the preparation as well as the preservation of clean and uniform samples is essential. In order to overcome these issues, we present a new method where an ultra-high vacuum set-up comprising a Nion UltraSTEM100, a laser, and a plasma source permits superior cleaning of the lattice and the introduction of the atomic-scale disorder. The reliable measurement of defects and their classification is accomplished through the synergetic use of our experimental framework with image recognition based on convolutional neural networks and an automatic routine for the acquisition of images, allowing an atomic-level characterization of defects from large sample areas.