Journal Article FZJ-2022-03546

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Automatic Alignment of an Orbital Angular Momentum Sorter in a Transmission Electron Microscope Using a Convolutional Neural Network

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2023
Cambridge University Press New York, NY

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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.

Classification:

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. Q-SORT - QUANTUM SORTER (766970) (766970)
  3. 3D MAGiC - Three-dimensional magnetization textures: Discovery and control on the nanoscale (856538) (856538)
  4. ESTEEM3 - Enabling Science and Technology through European Electron Microscopy (823717) (823717)
  5. DFG project 405553726 - TRR 270: Hysterese-Design magnetischer Materialien für effiziente Energieumwandlung (405553726) (405553726)
  6. DARPA, Phase 2 - Defense Advanced Research Projects Agency Manipulation of magnetic skyrmions for logicin- memory applications (Z1422.01.18) (Z1422.01.18)

Appears in the scientific report 2023
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 Record created 2022-09-29, last modified 2023-09-29