Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning
transmission
electron microscopy (STEM) data streams, based on 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 in graphene. The ELIT-based approach facilitates direct on-the-fly
analysis of the STEM data and engenders real-time feedback schemes
for probing electron beam chemistry, atomic manipulation, and atom
by atom assembly.