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New Techniques for the Analysis of Fine-Scaled Clustering Phenomena within Atom Probe Tomography (APT) Data

Published online by Cambridge University Press:  14 November 2007

Leigh T. Stephenson ,
Michael P. Moody ,
Peter V. Liddicoat  and
Simon P. Ringer
Leigh T. Stephenson
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Michael P. Moody
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Peter V. Liddicoat
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia
Simon P. Ringer
Affiliation:
Australian Key Centre for Microscopy and Microanalysis, University of Sydney, Madsen Building, F09, Sydney, NSW 2006, Australia ARC Centre for Design in Light Metals, The University of Sydney, Sydney, NSW 2006, Australia
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Abstract

Nanoscale atomic clusters in atom probe tomographic data are not universally defined but instead are characterized by the clustering algorithm used and the parameter values controlling the algorithmic process. A new core-linkage clustering algorithm is developed, combining fundamental elements of the conventional maximum separation method with density-based analyses. A key improvement to the algorithm is the independence of algorithmic parameters inherently unified in previous techniques, enabling a more accurate analysis to be applied across a wider range of material systems. Further, an objective procedure for the selection of parameters based on approximating the data with a model of complete spatial randomness is developed and applied. The use of higher nearest neighbor distributions is highlighted to give insight into the nature of the clustering phenomena present in a system and to generalize the clustering algorithms used to analyze it. Maximum separation, density-based scanning, and the core linkage algorithm, developed within this study, were separately applied to the investigation of fine solute clustering of solute atoms in an Al-1.9Zn-1.7Mg (at.%) at two distinct states of early phase decomposition and the results of these analyses were evaluated.

Keywords

atom probe tomographysolute clusteringcluster algorithmsnearest neighborsdata analysisdata mining
Type
Research Article
Information
Microscopy and Microanalysis , Volume 13 , Issue 6: Special Issue: Atom Probe Tomography , December 2007 , pp. 448 - 463
Copyright
© 2007 Microscopy Society of America

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References

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Stephenson et al

Figure 8. Clustering in A1-1.9Zn-1.7Mg aged for 3600 secs at 150 (degree) C data set. All Mg and Zn atoms are shown

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