Journal Article

FZJ-2023-00085

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network

Bertoni, G. (Corresponding author)Extern* ; Rotunno, E. (Corresponding author)Extern* ; Marsmans, D. ; Tiemeijer, P. ; Tavabi, A. H.FZJ* ; Dunin-Borkowski, R. E.FZJ* ; Grillo, V.

2023
Elsevier Science Amsterdam

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Please use a persistent id in citations: http://hdl.handle.net/2128/33366  doi:10.1016/j.ultramic.2022.113663

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.

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Contributing Institute(s):

1. Physik Nanoskaliger Systeme (ER-C-1)

Research Program(s):

1. 5351 - Platform for Correlative, In Situ and Operando Characterization (POF4-535) (POF4-535)
2. ESTEEM3 - Enabling Science and Technology through European Electron Microscopy (823717) (823717)

Appears in the scientific report 2023

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Database coverage:
; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Web of Science Core Collection

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