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Related Concept Videos

Transmission Electron Microscopy01:15

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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
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Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

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Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to

Marc Botifoll1, Ivan Pinto-Huguet1, Enzo Rotunno2

  • 1Catalan Institute of Nanoscience and Nanotechnology - ICN2 (CSIC and BIST), Campus UAB, Bellaterra, Barcelona, 08193, Catalonia, Spain.

Advanced Materials (Deerfield Beach, Fla.)
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

A new workflow automates electron microscopy analysis, rapidly correlating atomic structure with device function. This AI-driven approach creates digital twins for advanced materials simulations, accelerating scientific discovery.

Keywords:
artificial intelligenceautomationphysical modellingquantum materials and devicestransmission electron microscopy

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Condensed Matter Physics

Background:

  • Scanning transmission electron microscopy ((S)TEM) is crucial for materials science but struggles to link atomic structure to device properties due to time constraints.
  • Characterizing complex device heterostructures requires correlating crystallographic, compositional, and strain information with functional behavior.

Purpose of the Study:

  • To introduce an automated analytical workflow for holistic characterization, modeling, and simulation of device heterostructures.
  • To significantly reduce the time required for (S)TEM data analysis from days to minutes.
  • To enable the creation of digital twins for simulating device behavior and understanding structure-property relationships.

Main Methods:

  • Automated analysis of (S)TEM data for crystallographic information, 3D orientation, elemental composition, and strain distribution.
  • Application of a physics-guided artificial intelligence model for materials and sample description.
  • Generation of 3D finite element and atomic models (digital twins) for systems with translational invariance.

Main Results:

  • The workflow automates complex (S)TEM analysis in minutes, a task previously taking days.
  • Digital twins were created for SiGe planar heterostructures, enabling simulations of phononic, electronic, and spin properties.
  • Demonstrated correlation between atomic structure and functional properties, including spatially-resolved characteristics and spin orbit lengths.

Conclusions:

  • The developed workflow offers a rapid and comprehensive approach to characterizing device heterostructures.
  • The AI-driven digital twin generation facilitates crucial insights into device behavior and materials properties.
  • The workflow's versatility is confirmed across diverse materials, device configurations, and sample morphologies.