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Updated: Oct 30, 2025

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Alignment of electron optical beam shaping elements using a convolutional neural network.

E Rotunno1, A H Tavabi2, P Rosi3

  • 1Istituto di Nanoscienze - CNR, 41125 Modena, Italy.

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|July 4, 2021
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Summary
This summary is machine-generated.

A convolutional neural network precisely aligns an orbital angular momentum sorter in transmission electron microscopy. This accurate and fast method enables real-time tuning of electron optical devices and beam shaping.

Keywords:
Beam ShapingConvolution neural networkOrbital Angular Momentum SorterSelf-Alignment

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Area of Science:

  • Physics
  • Materials Science
  • Optics

Background:

  • Transmission electron microscopy (TEM) requires precise alignment of electron optical devices.
  • Orbital angular momentum (OAM) sorters offer advanced electron beam manipulation capabilities.
  • Manual alignment of OAM sorters is time-consuming and complex.

Purpose of the Study:

  • To develop an automated and accurate method for aligning an OAM sorter in a TEM.
  • To demonstrate the feasibility of using machine learning for electron optical device alignment.
  • To enable real-time adjustments for improved TEM performance.

Main Methods:

  • Implementation of a convolutional neural network (CNN) for image analysis and alignment control.
  • Utilizing simulated datasets and experimental data from a TEM.
  • Developing an algorithm for precise positioning of the OAM sorter.

Main Results:

  • The CNN achieved high accuracy in aligning the OAM sorter.
  • The alignment process was significantly faster compared to manual methods.
  • Successful demonstration of the method in both simulations and experimental TEM.

Conclusions:

  • Convolutional neural networks provide an effective solution for aligning OAM sorters in TEM.
  • The developed method facilitates real-time tuning, enhancing flexibility in electron microscopy.
  • This approach has potential for real-time tuning of other electron optical devices and beam shaping configurations.