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Convolutional neural network as a tool for automatic alignment of electron optical beam shaping devices

Published online by Cambridge University Press:  30 July 2021

Enzo Rotunno ,
Amir Tavabi ,
Paolo Rosi ,
Stefano Frabboni ,
Peter Tiemeijer ,
Rafal Dunin-Borkovski  and
Vincenzo Grillo
Enzo Rotunno
Affiliation:
CNR - Istituto di Nanoscienze Modena, United States
Amir Tavabi
Affiliation:
Forschungszentrum Juelich, United States
Paolo Rosi
Affiliation:
University of Modena and Reggio Emilia, United States
Stefano Frabboni
Affiliation:
CNR - Istituto di Nanoscienze Modena, United States
Peter Tiemeijer
Affiliation:
Thermo Fisher Scientific, Netherlands
Rafal Dunin-Borkovski
Affiliation:
Forschungszentrum Juelich, Jülich, Nordrhein-Westfalen, Germany
Vincenzo Grillo
Affiliation:
CNR - Istituto di Nanoscienze Modena, Modena, Emilia-Romagna, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 822 - 824
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Haider, M., Uhlemann, S., Schwan, E., Rose, H., Kabius, B., and Urban, K., Nature, 392 (1998), 768.CrossRefGoogle Scholar
Haider, M., Muller, H., Uhlemann, S., Zach, J., Loebau, U., Hoeschen, R., Ultramicroscopy 108 (2008), 167-178.CrossRefGoogle Scholar
Tiemeijer, P.C., Bischoff, M., Freitag, B., Kisielowski, C., Ultramicroscopy 114 (2012) 72-81.CrossRefGoogle Scholar
Verbeeck, J., Béché, A., Müller-Caspary, K., Guzzinati, G., Luong, M. A., Den Hertog, M., Ultramicroscopy, 190 (2018), 58–65.CrossRefGoogle Scholar
Tavabi, A. H., Larocque, H., Lu, P-H, Duchamp, M., Grillo, V., Karimi, E., Dunin-Borkowski, R. E., and Pozzi, G., Phys. Rev. Research, 2 (2020), 013185.CrossRefGoogle Scholar
Grillo, V., Karimi, E., Gazzadi, G.C., Frabboni, S., Dennis, M.R., Boyd, R.W., Phys. Rev. X, 4 (2014), 011013.Google Scholar
Grillo, V., Tavabi, A. H., Yucelen, E., Lu, P.-H., Venturi, F., Larocque, H., Jin, L., Savenko, A., Gazzadi, G. C., Balboni, R., Frabboni, S., Tiemeijer, P., Dunin-Borkowski, R.E., Karimi, E., Opt. Express, 25 (2017), 21851.Google Scholar
Tavabi, A.H., Rosi, P., Pozzi, G., Roncaglia, A., Frabboni, S., Rotunno, E., Lu, P-H, Nijland, R., Tiemeijer, P., Karimi, E., Dunin-Borkowski, R.E., Grillo, V., arXiv:1910.03706.Google Scholar
Simonyan, K., Zisserman, A., arXiv:1409.1556v6.Google Scholar
Weigert, M., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., Wilhelm, B., Schmidt, D., Broaddus, C., Culley, S., Rocha-Martins, M., Segovia-Miranda, F., Norden, C., Henriques, R., Zerial, M., Solimena, M., Rink, J., Tomancak, P., Royer, L., Jug, F., Myers, E.W., Nat. Methods, 15 (2018), 1090–1097.CrossRefGoogle Scholar
McMorran, B.J., Harvey, T.R., Lavery, M.P.J., New J. Phys. 19 (2017), 023053.CrossRefGoogle Scholar