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Refining Spatial Distribution Maps for Atom Probe Tomography via Data Dimensionality Reduction Methods

Published online by Cambridge University Press:  09 October 2012

Santosh K. Suram  and
Krishna Rajan
Santosh K. Suram
Affiliation:
Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
Krishna Rajan*
Affiliation:
Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
*
*Corresponding author. Email: [email protected]
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Abstract

A mathematical framework based on singular value decomposition is used to analyze the covariance among interatomic frequency distributions in spatial distribution maps (SDMs). Using this approach, singular vectors that capture the covariance within the SDM data are obtained. The structurally relevant singular vectors (SRSVs) are identified. Using the SRSVs, we extract information from z-SDMs that not only captures the offset between the atomic planes but also captures the covariance in the atomic structure among the neighborhood atomic planes. These refined z-SDMs classify the Δ(Δz) slices in the SDMs into structurally relevant information, noise, and aberrations. The SRSVs are used to construct refined xy-SDMs that provide enhanced structural information for three-dimensional atom probe tomography.

Keywords

atom probe tomography (APT)crystal structuresingular value decomposition (SVD)spatial distribution maps (SDM)singular vectorsrefined z-SDM and refined xy-SDM
Type
Techniques and Equipment Development
Information
Microscopy and Microanalysis , Volume 18 , Issue 5 , October 2012 , pp. 941 - 952
Copyright
Copyright © Microscopy Society of America 2012

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