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@ARTICLE{Bertoni:916761,
      author       = {Bertoni, Giovanni and Rotunno, Enzo and Marsmans, Daan and
                      Tiemeijer, Peter and Tavabi, Amir H. and Dunin-Borkowski,
                      Rafal E. and Grillo, Vincenzo},
      title        = {{N}ear-real-time diagnosis of electron optical phase
                      aberrations in scanning transmission electron microscopy
                      using an artificial neural network},
      journal      = {Ultramicroscopy},
      volume       = {245},
      issn         = {0304-3991},
      address      = {Amsterdam},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2023-00085},
      pages        = {113663 -},
      year         = {2023},
      abstract     = {The key to optimizing spatial resolution in a
                      state-of-the-art scanning transmission electron microscope
                      is the ability to measure and correct for electron optical
                      aberrations of the probe-forming lenses precisely. Several
                      diagnostic methods for aberration measurement and correction
                      have been proposed, albeit often at the cost of relatively
                      long acquisition times. Here, we illustrate how artificial
                      intelligence can be used to provide near-real-time diagnosis
                      of aberrations from individual Ronchigrams. The demonstrated
                      speed of aberration measurement is important because
                      microscope conditions can change rapidly. It is also
                      important for the operation of MEMS-based hardware
                      correction elements, which have less intrinsic stability
                      than conventional electromagnetic lenses.},
      cin          = {ER-C-1},
      ddc          = {570},
      cid          = {I:(DE-Juel1)ER-C-1-20170209},
      pnm          = {5351 - Platform for Correlative, In Situ and Operando
                      Characterization (POF4-535) / ESTEEM3 - Enabling Science and
                      Technology through European Electron Microscopy (823717)},
      pid          = {G:(DE-HGF)POF4-5351 / G:(EU-Grant)823717},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36566529},
      UT           = {WOS:000912355300001},
      doi          = {10.1016/j.ultramic.2022.113663},
      url          = {https://juser.fz-juelich.de/record/916761},
}