Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Boebinger, Matthew; Ghosh, Ayana; Roccapriore, Kevin; Misra, Sudhajit; Xiao, Kai; Jesse, Stephen; Ziatdinov, Maxim; Kalinin, Sergei V.; Unocic, Raymond

doi:10.1038/s41524-024-01448-7

Title: Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Journal Article · 14 November 2024 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-024-01448-7 · OSTI ID:2478371

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2478371

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Vol. 10; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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