Probing electron beam induced transformations on a single defect level via automated scanning transmission electron microscopy

TL;DR

This paper introduces an automated, real-time analysis method using deep learning for STEM data, enabling atomic-level manipulation and defect engineering in materials under electron beam irradiation.

Contribution

It presents a novel ELIT-based deep learning approach integrated with beam control for automated atomic manipulation and defect engineering in STEM.

Findings

Successful atomically precise engineering of vacancy lines in TMDs

Creation and identification of topological defects in graphene

Real-time analysis enabling feedback-controlled atomic manipulation

Abstract

The robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on the ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects graphene. The ELIT-based approach opens the pathway toward the direct on-the-fly analysis of the STEM data and engendering real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.