Informatics for Quantitative Analysis of Atom Probe Tomography Images

2009 ◽  
Vol 1231 ◽  
Author(s):  
Santosh K Suram ◽  
Krishna Rajan
Keyword(s):  
Data Mining ◽  
Spatial Distribution ◽  
Quantitative Analysis ◽  
Atom Probe Tomography ◽  
Structural Information ◽  
Three Dimensional ◽  
Atomic Plane ◽  
Atom Probe ◽  
Distribution Maps

AbstractAn informatics based approach to extract further refinements on the crystallographic information embedded in the Spatial Distribution Maps (SDMs) has been developed. The data mining based methods to generate and interpret spectra that de-convolute the SDMs are discussed. This work has resulted in a method to generate SDMs that can map three-dimensional crystallographic information as opposed to existing methods that map structural information on only one atomic plane at a time. The broader implications of this work on enhancing the interpretation and resolution of structural information in atom probe tomography studies is also discussed.

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

2012 ◽  
Vol 18 (5) ◽  
pp. 941-952 ◽  
Author(s):  
Santosh K. Suram ◽  
Krishna Rajan
Keyword(s):  
Spatial Distribution ◽  
Atom Probe Tomography ◽  
Structural Information ◽  
Three Dimensional ◽  
Relevant Information ◽  
Atom Probe ◽  
Mathematical Framework ◽  
Frequency Distributions ◽  
Distribution Maps ◽  
Singular Vectors

AbstractA 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.

Spatial Distribution Maps for Atom Probe Tomography

2007 ◽  
Vol 13 (6) ◽  
pp. 437-447 ◽  
Author(s):  
Brian P. Geiser ◽  
Thomas F. Kelly ◽  
David J. Larson ◽  
Jason Schneir ◽  
Jay P. Roberts
Keyword(s):  
Spatial Distribution ◽  
Atom Probe Tomography ◽  
Detection Efficiency ◽  
Structural Information ◽  
Atom Probe ◽  
Dark Field ◽  
Distribution Functions ◽  
Orientation Relationships ◽  
Distribution Maps ◽  
Space Technique

A real-space technique for finding structural information in atom probe tomographs, spatial distribution maps (SDM), is described. The mechanics of the technique are explained, and it is then applied to some test cases. Many applications of SDM in atom probe tomography are illustrated with examples including finding crystal lattices, correcting lattice strains in reconstructed images, quantifying trajectory aberrations, quantifying spatial resolution, quantifying chemical ordering, dark-field imaging, determining orientation relationships, extracting radial distribution functions, and measuring ion detection efficiency.

Spatial Distribution Maps for Atom Probe Tomography

2006 ◽  
Author(s):  
B. P. Geiser ◽  
J. Schneir ◽  
J. Roberts ◽  
S. Wiener ◽  
D. J. Larson ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Distribution Maps

Quantitative analysis of Si/SiGeC superlattices using atom probe tomography

2015 ◽  
Vol 159 ◽  
pp. 223-231 ◽  
Author(s):  
Robert Estivill ◽  
Adeline Grenier ◽  
Sébastien Duguay ◽  
François Vurpillot ◽  
Tanguy Terlier ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe

Challenges Associated with the Characterisation of Nanocrystalline Materials Using Atom Probe Tomography

2010 ◽  
Vol 654-656 ◽  
pp. 2366-2369 ◽  
Author(s):  
Feng Zai Tang ◽  
Talukder Alam ◽  
Michael P. Moody ◽  
Baptiste Gault ◽  
Julie M. Cairney
Keyword(s):  
Quantitative Analysis ◽  
Atom Probe Tomography ◽  
Nanocrystalline Materials ◽  
Atom Probe ◽  
Atomic Scale ◽  
Three Dimensions ◽  
Specimen Preparation ◽  
Compositional Information

Atom probe tomography provides compositional information in three dimensions at the atomic scale, and is therefore extremely suited to the study of nanocrystalline materials. In this paper we present atom probe results from the investigation of nanocomposite TiSi¬Nx coatings and nanocrystalline Al. We address some of the major challenges associated with the study of nanocrystalline materials, including specimen preparation, visualisation, common artefacts in the data and approaches to quantitative analysis. We also discuss the potential for the technique to relate crystallographic information to the compositional maps.

scholarly journals Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yue Li ◽  
Xuyang Zhou ◽  
Timoteo Colnaghi ◽  
Ye Wei ◽  
Andreas Marek ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Chemical Species ◽  
Atom Probe ◽  
Local Order ◽  
Face Centered Cubic ◽  
Ordered Structure ◽  
Ordered Structures ◽  
Distribution Maps ◽  
Structure Recognition ◽  
Fcc Alloys

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

scholarly journals High-Accuracy Sample Preparation for Three Dimensional Atom Probe Tomography Using Orthogonal Column Layout FIB-SEM and its STEM function

2018 ◽  
Vol 24 (S1) ◽  
pp. 830-831
Author(s):  
Miki Tsuchiya ◽  
Yoshihisa Orai ◽  
Takahiro Sato ◽  
Xin Man ◽  
Junichi Katane ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Atom Probe Tomography ◽  
Three Dimensional ◽  
Atom Probe ◽  
High Accuracy

scholarly journals Quantitative Analysis of Cementite in Steel by Atom Probe Tomography

2009 ◽  
Vol 15 (S2) ◽  
pp. 306-307
Author(s):  
J Takahashi ◽  
K Kawakami ◽  
Y Yamaguchi
Keyword(s):  
Quantitative Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe

Extended abstract of a paper presented at Microscopy and Microanalysis 2009 in Richmond, Virginia, USA, July 26 – July 30, 2009

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