TY  - JOUR
AU  - Bertoni, Giovanni
AU  - Rotunno, Enzo
AU  - Marsmans, Daan
AU  - Tiemeijer, Peter
AU  - Tavabi, Amir H.
AU  - Dunin-Borkowski, Rafal E.
AU  - Grillo, Vincenzo
TI  - Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network
JO  - Ultramicroscopy
VL  - 245
SN  - 0304-3991
CY  - Amsterdam
PB  - Elsevier Science
M1  - FZJ-2023-00085
SP  - 113663 -
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 36566529
UR  - <Go to ISI:>//WOS:000912355300001
DO  - DOI:10.1016/j.ultramic.2022.113663
UR  - https://juser.fz-juelich.de/record/916761
ER  -