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Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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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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Related Experiment Video

Updated: Nov 21, 2025

Measuring the Complete-arch Distortion of an Optical Dental Impression
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Evaluation of image distortion in SEM by using a dot-array-based certified reference material.

Kazuhiro Kumagai1, Akira Kurokawa1

  • 1National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 5, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8565, Japan.

Microscopy (Oxford, England)
|January 16, 2021
PubMed
Summary

A new scanning electron microscopy method uses certified reference materials to quickly evaluate image distortion. This technique enhances measurement reliability by quantifying distortion and its uncertainty.

Keywords:
SI traceabilitycertified reference materialdot-arrayimage distortionmetrologyscanning electron microscopy

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Area of Science:

  • Materials Science
  • Metrology
  • Microscopy

Background:

  • Scanning electron microscopy (SEM) is crucial for high-resolution imaging and dimensional measurements.
  • Image distortion in SEM can significantly impact measurement accuracy and reliability.
  • Accurate evaluation of distortion is essential for metrological applications.

Purpose of the Study:

  • To develop a simple and rapid method for assessing image distortion in SEM.
  • To enable quantitative evaluation of distortion significance using uncertainty analysis.
  • To improve the overall reliability of measurements performed with SEM.

Main Methods:

  • Utilizing a certified reference material (CRM) with a known dot-array structure.
  • Performing image analysis to measure distances between dots in the CRM.
  • Calculating variations in dot intervals to detect image distortion.
  • Incorporating the uncertainty of the CRM's certified value for quantitative assessment.

Main Results:

  • The method effectively detects image distortion by analyzing variations in dot pitch.
  • Quantitative evaluation of distortion significance is achievable by considering measurement uncertainty.
  • The procedure is quick and straightforward, suitable for routine use.

Conclusions:

  • This method provides a reliable way to estimate uncertainty arising from image distortion in SEM.
  • It enhances the trustworthiness of dimensional measurements obtained from SEM.
  • The approach offers a practical solution for quality control in SEM imaging.