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Updated: May 16, 2026

Atom Probe Tomography Analysis of Exsolved Mineral Phases
08:14

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Published on: October 25, 2019

Point process statistics in atom probe tomography.

T Philippe1, S Duguay, G Grancher

  • 1Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.

Ultramicroscopy
|December 15, 2012
PubMed
Summary
This summary is machine-generated.

Spatial point processes offer advanced statistical analysis for atom probe tomography (APT) data, enabling accurate atomic distribution assessment without sampling. These methods effectively analyze microelectronics materials, revealing chemical composition and cluster characteristics.

Keywords:
Atom probe tomographyClusteringDelaunay tessellationNearest neighbourPair correlation functionSpatial point process

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Last Updated: May 16, 2026

Atom Probe Tomography Analysis of Exsolved Mineral Phases
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Atom Probe Tomography Studies on the Cu(In,Ga)Se2 Grain Boundaries
09:51

Atom Probe Tomography Studies on the Cu(In,Ga)Se2 Grain Boundaries

Published on: April 22, 2013

Area of Science:

  • Materials Science
  • Statistical Modeling
  • Data Analysis

Background:

  • Atom Probe Tomography (APT) generates complex, high-resolution 3D atomic data.
  • Analyzing atomic distributions and chemical composition in APT datasets presents significant challenges.
  • Existing methods may require sampling or lack robust statistical frameworks for detailed analysis.

Purpose of the Study:

  • To review and present spatial point processes as effective statistical models for APT data analysis.
  • To introduce methods for assessing atomic distribution, detecting deviations from randomness, and evaluating chemical composition.
  • To demonstrate the application of these statistical tools in microelectronics materials research.

Main Methods:

  • Utilizing spatial point processes as a statistical modeling framework for APT data.
  • Employing the mean distance to the nearest neighbor to characterize atomic distribution.
  • Developing a chi-squared (χ(2)) test for detecting non-randomness in atomic arrangements.
  • Applying best-fit methods based on first nearest neighbor (1 NN) distance and pair correlation functions.
  • Illustrating Delaunay tessellation for precise cluster identification and selection.

Main Results:

  • Spatial point processes provide a robust, non-sampling approach for APT data analysis.
  • The nearest neighbor distance and χ(2) test effectively identify non-random atomic distributions.
  • 1 NN method and pair correlation functions accurately assess the chemical composition of small clusters.
  • Delaunay tessellation aids in the accurate selection and analysis of atomic clusters.
  • Demonstrated successful application to APT experiments on microelectronics.

Conclusions:

  • Spatial point processes represent a powerful statistical toolkit for advancing APT data interpretation.
  • These methods enhance the ability to quantify atomic clustering and chemical heterogeneity.
  • The presented statistical approaches are valuable for materials science research, particularly in microelectronics.