Automatic Alignment of an Orbital Angular Momentum Sorter in a Transmission Electron Microscope Using a Convolutional Neural Network
Source abstract
Abstract
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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Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Electron and X-Ray Spectroscopy Techniques · Near-Field Optical Microscopy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Istituto Nanoscienze - CNR , via G. Campi 213/A, Modena 41125 , Italy
FIM Department, University of Modena and Reggio Emilia , via G. Campi 213/A, Modena 41125 , Italy
Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons, Forschungszentrum Jülich , Jülich 52425 , Germany
Thermo Fisher Scientific , PO Box 80066, KA Eindhoven 5600 , The Netherlands
Funding
DARPA TEE · Deutsche Forschungsgemeinschaft
References (63)
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