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

TL;DR

This paper presents a convolutional neural network approach to accurately and rapidly align electron optical beam shaping elements, demonstrated through simulations and experiments, enabling real-time tuning of electron devices.

Contribution

The paper introduces a CNN-based method for aligning electron optical elements, improving speed and accuracy over traditional techniques.

Findings

CNN achieves high alignment accuracy

Method is effective in real-time tuning

Validated through simulations and experiments

Abstract

A convolutional neural network is used to align an orbital angular momentum sorter in a transmission electron microscope. The method is demonstrated using simulations and experimentally. As a result of its accuracy and speed, it offers the possibility of real-time tuning of other electron optical devices and electron beam shaping configurations.