Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network

TL;DR

This paper presents a neural network-based method for near-real-time diagnosis of electron optical aberrations in scanning transmission electron microscopy, enabling faster and more accurate correction of lens imperfections.

Contribution

It introduces an artificial intelligence approach that significantly reduces the time needed to diagnose aberrations, improving the responsiveness of microscopy correction systems.

Findings

Neural network accurately diagnoses aberrations from individual Ronchigrams

Method achieves near-real-time speed for aberration measurement

Potential to enhance stability and resolution in electron microscopy

Abstract

The key to optimizing spatial resolution in a state-of-the-art scanning transmission electron microscope is the ability to precisely measure and correct for electron optical aberrations of the probe-forming lenses. Several diagnostic methods for aberration measurement and correction with maximum precision and accuracy have been proposed, albeit often at the cost of relatively long acquisition times. Here, we illustrate how artificial intelligence can be used to provide near-real-time diagnosis of aberrations from individual Ronchigrams. The demonstrated speed of aberration measurement is important as microscope conditions can change rapidly, as well as for the operation of MEMS-based hardware correction elements that have less intrinsic stability than conventional electromagnetic lenses.