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Scanning Electron Microscopy

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
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Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Electron tomography can be performed either in TEM or STEM (scanning transmission...

Overview of Microscopy Techniques

Overview of Microscopy Techniques

The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...

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Related Experiment Video

Updated: Oct 21, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method.

Lun Zhao^1,2, Yunlong Pan³, Sen Wang³

¹Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic, Shenzhen 518055, China.

|September 2, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning method for identifying microcracks in scanning electron microscope (SEM) images. The approach achieves 71.12% detection accuracy, successfully identifying cracks across various magnifications and backgrounds.

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Scanning electron microscopy (SEM) is crucial for material analysis, including fracture and microstructure studies.
Advancements in materials science and computer vision necessitate improved detection technologies.
Microcrack identification in SEM images is challenging due to complex microscopic topography.

Purpose of the Study:

To develop an intelligent method for microcrack identification in SEM images using deep learning.
To enhance the accuracy and robustness of microcrack detection in complex material microstructures.
To address limitations in current SEM image analysis techniques for defect detection.

Main Methods:

Utilized a deep learning model at the image level to minimize interference from background topography.
Proposed a novel detection method employing dense, continuous bounding boxes tailored for SEM images.
Implemented bounding box rotation to reduce feature variations and enhance local crack feature extraction.

Main Results:

Achieved a detection accuracy of 71.12% for microcracks in SEM images.
Obtained a maximum mean Intersection over Union (mIOU) of 64.13%.
Successfully detected microcracks across diverse magnifications and background conditions.

Conclusions:

The proposed deep learning approach effectively identifies microcracks in SEM images.
The method demonstrates robustness in handling variations in magnification and background complexity.
This technique offers a significant advancement in automated defect analysis for materials science.