ON THE STATISTICAL ANALYSIS OF ATOM PROBE DATA

1987 ◽  
Vol 48 (C6) ◽  
pp. C6-559-C6-564 ◽  
Author(s):  
M. G. Hetherington ◽  
M. K. Miller
Keyword(s):  
Statistical Analysis ◽  
Atom Probe

STATISTICAL ANALYSIS OF ATOM PROBE DATA

1986 ◽  
Vol 47 (C7) ◽  
pp. C7-489-C7-494
Author(s):  
L. V. ALVENSLEBEN ◽  
R. GRÜNE ◽  
A. HÜTTEN ◽  
M. OEHRING
Keyword(s):  
Statistical Analysis ◽  
Atom Probe

scholarly journals Applying Multivariate Statistical Analysis to Atom Probe Tomography

2010 ◽  
Vol 16 (S2) ◽  
pp. 1858-1859 ◽  
Author(s):  
C Parish ◽  
C Capdevila ◽  
MK Miller
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Multivariate Statistical

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.

Atomic-Scale Phase Composition through Multivariate Statistical Analysis of Atom Probe Tomography Data

2011 ◽  
Vol 17 (3) ◽  
pp. 418-430 ◽  
Author(s):  
Michael R. Keenan ◽  
Vincent S. Smentkowski ◽  
Robert M. Ulfig ◽  
Edward Oltman ◽  
David J. Larson ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Atomic Scale ◽  
Single Atom ◽  
Chemical Information ◽  
Laptop Computer ◽  
Multivariate Statistical ◽  
Tomography Data

AbstractWe demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

STATISTICAL ANALYSIS OF ATOM PROBE DATA

1986 ◽  
Vol 47 (C7) ◽  
pp. C7-495-C7-501 ◽  
Author(s):  
M. G. HETHERINGTON ◽  
A. CEREZO ◽  
J. HYDE ◽  
G. D.W. SMITH ◽  
G. M. WORRALL
Keyword(s):  
Statistical Analysis ◽  
Atom Probe

scholarly journals Phase Composition at the Atomic-Size Scale through Multivariate Statistical Analysis of Atom Probe Tomography Data

2011 ◽  
Vol 17 (S2) ◽  
pp. 720-721
Author(s):  
M Keenan ◽  
V Smentkowski ◽  
R Ulfig ◽  
E Oltman ◽  
D Larson ◽  
...  
Keyword(s):  
Phase Composition ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Multivariate Statistical ◽  
Atomic Size ◽  
Size Scale ◽  
Tomography Data

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

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