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Related Experiment Video

Updated: Oct 15, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Robust autofocusing for scanning electron microscopy based on a dual deep learning network.

Woojin Lee¹, Hyeong Soo Nam¹, Young Gon Kim²

¹Department of Mechanical Engineering, KAIST, Daejeon, 34141, Republic of Korea.

Scientific Reports

|October 23, 2021

Summary

This summary is machine-generated.

This study introduces an automated autofocus system for scanning electron microscopy (SEM) using dual deep learning networks. This innovation simplifies SEM operation, making high-resolution imaging more accessible for various scientific applications.

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Materials Science
Nanotechnology
Microscopy

Background:

Scanning electron microscopy (SEM) offers high-resolution imaging but requires complex operation by trained personnel.
Adjusting numerous parameters for optimal image quality is a significant barrier to SEM accessibility.

Purpose of the Study:

To develop an automated autofocus method for SEM systems.
To broaden the usability of SEM technology by simplifying image acquisition.

Main Methods:

Implementation of a dual deep learning network comprising an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet).
AENet evaluates image quality, while ACNet controls SEM focus in real-time across different magnifications and sample positions.

Main Results:

The dual network system demonstrated successful autofocus performance on trained samples.
The method proved robust, achieving effective autofocusing on previously unseen samples.

Conclusions:

The proposed autofocusing system significantly enhances SEM versatility.
This deep learning-based approach is potentially applicable to a wide range of microscopy techniques.