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

作　　者：Matthew G.Boebinger Ayana Ghosh Kevin M.Roccapriore Sudhajit Misra Kai Xiao Stephen Jesse Maxim Ziatdinov Sergei V.Kalinin Raymond R.Unocic 

机构地区：[1]Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN,USA [2]Computational Science and Engineering Division,Oak Ridge National Laboratory,Oak Ridge,TN,USA [3]Physical Sciences Division,Pacific Northwest National Laboratory,Richland,WA,USA [4]Department of Materials Science and Engineering,University of Tennessee,Knoxville,TN,USA

出　　处：《npj Computational Materials》2024年第1期469-478,共10页计算材料学(英文)

基　　金：supported by the U.S.Department of Energy,Office of Basic Energy Sciences,Division of Materials Sciences and Engineering;This manuscript has been authored by UT-Battelle,LLC,under Contract No.DE-AC0500OR22725 with the U.S.

摘　　要：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 scanSTEMdata,up to 100 fps,to track the atomic fabrication process during nanopore milling in monolayer MoS_(2).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.

关 键 词：BEAM DEFECT HIGH 

分 类 号：TG1[金属学及工艺—金属学]