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Deep PACBED: Multitask analysis of PACBED images using deep neural networks.

Daniel Schneider1, Jonas Scheunert2, Damien Heimes2

  • 1Department of Mathematics & Computer Science, Marburg University, Hans-Meerwein-Straße 6, 35032, Marburg, Germany.

Ultramicroscopy
|June 25, 2026
PubMed
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Multitask deep neural networks can rapidly and accurately analyze scanning transmission electron microscopy (STEM) data, measuring semiconductor sample parameters like thickness, rotation, and mistilt. This machine learning approach improves upon traditional manual methods for large datasets.

Area of Science:

  • Materials Science
  • Data Science
  • Physics

Background:

  • Scanning transmission electron microscopes (STEMs) generate large datasets of semiconductor images.
  • Position-averaged convergent beam electron diffraction (PACBED) images are valuable for determining sample parameters.
  • Manual measurement of these parameters is time-consuming and prone to inaccuracies.

Purpose of the Study:

  • To investigate multitask deep neural networks for simultaneous analysis of multiple sample parameters from PACBED images.
  • To compare the performance of multitask models against single-parameter models.
  • To explore the impact of experimental data quantity on model performance.

Main Methods:

  • Development and training of multitask deep neural networks.
Keywords:
4DSTEMDeep learningDomain shiftImage analysisMultitask learningPACBED

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  • Utilizing synthetic PACBED images (III-V semiconductors, silicon) simulated via a multi-slice algorithm.
  • Incorporating small amounts of experimental data for training and validation.
  • Evaluating model performance based on mean absolute error for thickness, rotation, and mistilt, and accuracy for material classification.

    • Main Results:

    • The best deep neural network achieved high accuracy: 4.19° for rotation (α), 0.43 milliradians for mistilt (β), and 2.93 nanometers for thickness (t).
    • These error margins are comparable to experimental measurement uncertainties.
    • Multitask models outperformed single-parameter models.
    • Material classification was performed without errors on trained materials.

    Conclusions:

    • Multitask deep learning offers a rapid, consistent, and accurate method for analyzing large PACBED datasets from STEM.
    • This approach significantly enhances the efficiency and reliability of semiconductor sample characterization.
    • The findings suggest a promising direction for automated analysis in electron microscopy.