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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: Dec 26, 2025

A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy
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A Virtual Simulation Experiment of Mechanics: Material Deformation and Failure Based on Scanning Electron Microscopy

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An a posteriori Error Estimate for Scanning Electron Microscope Simulation with Adaptive Mesh Refinement.

William F Mitchell1, John S Villarrubia2

  • 1Applied and Computational Mathematics Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899.

Journal of Scientific Computing
|March 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new adaptive mesh refinement algorithm for scanning electron microscope simulations. It significantly reduces computation time and mesh size while maintaining accuracy in electron trajectory simulations.

Keywords:
A posteriori error estimationAdaptive mesh refinementFinite element analysisScanning electron microscope simulation

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Area of Science:

  • Materials Science
  • Computational Physics
  • Microscopy

Background:

  • Scanning electron microscopy (SEM) signal intensity depends complexly on sample topography and composition.
  • Accurate SEM measurements require explicit accounting for the signal-measurand relationship.
  • Simulators are crucial for theoretical understanding due to numerous signal determinants.

Purpose of the Study:

  • To develop a novel adaptive mesh refinement algorithm for SEM simulations.
  • To improve the efficiency of finite element analysis in SEM simulations, particularly for charging nonconducting samples.
  • To minimize errors in electron trajectories during SEM simulations.

Main Methods:

  • Implementation of a new a posteriori local error estimator for adaptive mesh refinement.
  • Finite element analysis to compute electric fields in SEM simulations.
  • Algorithm validation using a test problem with a known exact solution.

Main Results:

  • The new adaptive mesh refinement algorithm reduces computation time and mesh size.
  • Achieved comparable electron trajectory accuracy to hand-graded meshes with fewer vertices.
  • Demonstrated a 3.5x reduction in vertices and 2.25x less computation time.

Conclusions:

  • The developed algorithm enhances the efficiency of SEM simulations.
  • Minimizing electron trajectory error is a more effective strategy for SEM simulations than traditional energy norm minimization.
  • This method offers significant computational savings for SEM simulations involving nonconducting samples.