  Directed atomic fabrication using an aberration-corrected scanning
transmission electron microscope (STEM) opens new pathways for atomic
engineering of functional materials. In this approach, the electron beam is
used to actively alter the atomic structure through electron beam induced
irradiation processes. One of the impediments that has limited widespread use
thus far has been the ability to understand the fundamental mechanisms of
atomic transformation pathways at high spatiotemporal resolution. Here, we
develop a workflow for obtaining and analyzing high-speed spiral scan STEM
data, up to 100 fps, to track the atomic fabrication process during nanopore
milling in monolayer MoS2. An automated feedback-controlled electron beam
positioning system combined with deep convolution neural network (DCNN) was
used to decipher fast but low signal-to-noise datasets and classify
time-resolved atom positions and nature of their evolving atomic defect
configurations. Through this automated decoding, the initial atomic disordering
and reordering processes leading to nanopore formation was able to be studied
across various timescales. Using these experimental workflows a greater degree
of speed and information can be extracted from small datasets without
compromising spatial resolution. This approach can be adapted to other 2D
materials systems to gain further insights into the defect formation necessary
to inform future automated fabrication techniques utilizing the STEM electron
beam.
