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

Published online by Cambridge University Press:  31 January 2019

Daniel Haley ,
Paul A. J. Bagot  and
Michael P. Moody
Daniel Haley*
Affiliation:
Department of Materials, Oxford University, 16 Parks Road, Oxford, OX1 3PH, UK
Paul A. J. Bagot
Affiliation:
Department of Materials, Oxford University, 16 Parks Road, Oxford, OX1 3PH, UK
Michael P. Moody
Affiliation:
Department of Materials, Oxford University, 16 Parks Road, Oxford, OX1 3PH, UK
*
*Author for correspondence: Daniel Haley, E-mail: daniel.haley@materials.ox.ac.uk
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Abstract

We 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.

Keywords

atom probecomputer programcrystallographydata analysis
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 331 - 337
Copyright
Copyright © Microscopy Society of America 2019 

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