Lattice Rectification in Atom Probe Tomography: Toward True Three-Dimensional Atomic Microscopy

AbstractAtom probe tomography (APT) represents a significant step toward atomic resolution microscopy, analytically imaging individual atoms with highly accurate, though imperfect, chemical identity and three-dimensional (3D) positional information. Here, a technique to retrieve crystallographic information from raw APT data and restore the lattice-specific atomic configuration of the original specimen is presented. This lattice rectification technique has been applied to a pure metal, W, and then to the analysis of a multicomponent Al alloy. Significantly, the atoms are located to their true lattice sites not by an averaging, but by triangulation of each particular atom detected in the 3D atom-by-atom reconstruction. Lattice rectification of raw APT reconstruction provides unprecedented detail as to the fundamental solute hierarchy of the solid solution. Atomic clustering has been recognized as important in affecting alloy behavior, such as for the Al-1.1Cu-1.7Mg (at. %) investigated here, which exhibits a remarkable rapid hardening reaction during the early stages of aging, linked to clustering of solutes. The technique has enabled lattice-site and species-specific radial distribution functions, nearest-neighbor analyses, and short-range order parameters, and we demonstrate a characterization of solute-clustering with unmatched sensitivity and precision.

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Clustering and Local Magnification Effects in Atom Probe Tomography: A Statistical Approach

Microscopy and Microanalysis ◽

10.1017/s1431927610000449 ◽

2010 ◽

Vol 16 (5) ◽

pp. 643-648 ◽

Cited By ~ 28

Author(s):

Thomas Philippe ◽

Maria Gruber ◽

François Vurpillot ◽

D. Blavette

Keyword(s):

Experimental Data ◽

Atom Probe Tomography ◽

Statistical Approach ◽

Nearest Neighbor ◽

Three Dimensional ◽

Atom Probe ◽

Quantitative Composition ◽

Second Phase ◽

Real Experimental Data ◽

Open Issues

AbstractLocal magnification effects and trajectory overlaps related to the presence of a second phase (clusters) are key problems and still open issues in the assessment of quantitative composition data in three-dimensional atom probe tomography (APT) particularly for tiny solute-enriched clusters. A model based on the distribution of distance of first nearest neighbor atoms has been developed to exhibit the variations in the apparent atomic density in reconstructed volumes and to correct compositions that are biased by local magnification effects. This model was applied to both simulated APT reconstructions and real experimental data and shows an excellent agreement with the expected composition of clusters.

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DF-Fit: A Robust Algorithm for Detection of Crystallographic Information in Atom Probe Tomography Data

Microscopy and Microanalysis ◽

10.1017/s1431927618015507 ◽

2019 ◽

Vol 25 (2) ◽

pp. 331-337

Author(s):

Daniel Haley ◽

Paul A. J. Bagot ◽

Michael P. Moody

Keyword(s):

Fourier Transform ◽

Spatial Data ◽

Atom Probe Tomography ◽

Computational Cost ◽

Three Dimensional ◽

Atom Probe ◽

Real Space ◽

Distribution Functions ◽

Quality Data ◽

Error Metric

AbstractWe report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods.By examining traditional DFs in an automated fashion in real space, rather than using Fourier transform approaches, we utilize an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artifacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost.This extension is demonstrated on acquired aluminum and tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses.

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