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Ultramicroscopy. 2013 Sep;132:114-20. doi: 10.1016/j.ultramic.2012.10.004. Epub 2012 Nov 6.

Point process statistics in atom probe tomography.

Author information

  • 1Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA. Electronic address: thomas.philippe1984@gmail.com.

Abstract

We present a review of spatial point processes as statistical models that we have designed for the analysis and treatment of atom probe tomography (APT) data. As a major advantage, these methods do not require sampling. The mean distance to nearest neighbour is an attractive approach to exhibit a non-random atomic distribution. A χ(2) test based on distance distributions to nearest neighbour has been developed to detect deviation from randomness. Best-fit methods based on first nearest neighbour distance (1 NN method) and pair correlation function are presented and compared to assess the chemical composition of tiny clusters. Delaunay tessellation for cluster selection has been also illustrated. These statistical tools have been applied to APT experiments on microelectronics materials.

Copyright © 2012 Elsevier B.V. All rights reserved.

KEYWORDS:

Atom probe tomography; Clustering; Delaunay tessellation; Nearest neighbour; Pair correlation function; Spatial point process

PMID:
23237770
[PubMed]
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