Hauptseite > Publikationsdatenbank > Automatic Alignment of an Orbital Angular Momentum Sorter in a Transmission Electron Microscope Using a Convolutional Neural Network |
Journal Article | FZJ-2022-03546 |
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2023
Cambridge University Press
New York, NY
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Please use a persistent id in citations: http://hdl.handle.net/2128/33923 doi:10.1017/S143192762201248X
Abstract: We report on the automatic alignment of a transmission electron microscope equipped with an orbital angular momentum sorter using a convolutional neural network. The neural network is able to control all relevant parameters of both the electron-optical setup of the microscope and the external voltage source of the sorter without input from the user. It can compensate for mechanical and optical misalignments of the sorter, in order to optimize its spectral resolution. The alignment is completed over a few frames and can be kept stable by making use of the fast fitting time of the neural network.
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