Automatic alignment of an orbital angular momentum sorter in a transmission electron microscope using a convolution neural network
Instrumentation and Detectors
2022-10-18 v2 Quantum Physics
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 is able to 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.
Cite
@article{arxiv.2111.05032,
title = {Automatic alignment of an orbital angular momentum sorter in a transmission electron microscope using a convolution neural network},
author = {P. Rosi and A. Clausen and D. Weber and A. H. Tavabi and S. Frabboni and P. Tiemeijer and R. E. Dunin-Borkowski and E. Rotunno and V. Grillo},
journal= {arXiv preprint arXiv:2111.05032},
year = {2022}
}