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Enhancing Element Identification by Expectation–Maximization Method in Atom Probe Tomography

Published online by Cambridge University Press:  28 February 2019

Francois Vurpillot ,
Constantinos Hatzoglou ,
Bertrand Radiguet ,
Gerald Da Costa ,
Fabien Delaroche  and
Frederic Danoix
Francois Vurpillot*
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
Constantinos Hatzoglou
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
Bertrand Radiguet
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
Gerald Da Costa
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
Fabien Delaroche
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
Frederic Danoix
Affiliation:
Normandie Université, UNIROUEN, INSA Rouen, CNRS, Groupe de Physique des Matériaux, 76000 Rouen, France
*
*Author for correspondence: Francois Vurpillot, E-mail: francois.vurpillot@univ-rouen.fr
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Abstract

This paper describes an alternative way to assign elemental identity to atoms collected by atom probe tomography (APT). This method is based on Bayesian assignation of label through the expectation–maximization method (well known in data analysis). Assuming the correct shape of mass over charge peaks in mass spectra, the probability of each atom to be labeled as a given element is determined, and is used to enhance data visualization and composition mapping in APT analyses. The method is particularly efficient for small count experiments with a low signal to noise ratio, and can be used on small subsets of analyzed volumes, and is complementary to single-ion decomposition methods. Based on the selected model and experimental examples, it is shown that the method enhances our ability to observe and extract information from the raw dataset. The experimental case of the superimposition of the Si peak and N peak in a steel is presented.

Keywords

atom probe tomographydata mininggeneral mixture modelsmass spectrum
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 367 - 377
Copyright
Copyright © Microscopy Society of America 2019 

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References

Blavette, D & Sauvage, X (2016). Early developments and basic concepts. In Atom Probe Tomography, pp. 1–15. Elsevier. http://linkinghub.elsevier.com/retrieve/pii/B9780128046470000012 (Accessed July 10, 2018).Google Scholar
Cadel, E, Fraczkiewicz, A & Blavette, D (2001). Atomic scale observation of Cottrell atmospheres in B-doped FeAl (B2) by 3D atom probe field ion microscopy. Mat Sci Eng A 309–310, 32–37.Google Scholar
Da Costa, G, Vurpillot, F, Bostel, A, Bouet, M & Deconihout, B (2005). Design of a delay-line position-sensitive detector with improved performance. Rev Sci Instrum 76, 013304.Google Scholar
Dempster, AP, Laird, NM & Rubin, DB (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc. Series B (Methodological) 39(1), 1–38. JSTOR, http://www.jstor.org/stable/2984875Google Scholar
Dijkstra, M, Roelofsen, H, Vonk, RJ & Jansen, RC (2006). Peak quantification in surface-enhanced laser desorption/ionization by using mixture models. PROTEOMICS 6, 5106–5116.Google Scholar
Figueiredo, F & Gomes, I (2013). The skew normal distribution in SPC. REVSTAT – Stat J 11, 83–104.Google Scholar
Gault, B (ed.) (2012). Atom Probe Microscopy. New York: Springer.Google Scholar
Goodman, SR, Brenner, SS & Low, JR (1973). An FIM-atom probe study of the precipitation of copper from iron-1.4 at. pct copper. Part II: Atom probe analyses. Metallurgical Trans 4, 2371–2378.Google Scholar
Haley, D, Choi, P & Raabe, D (2015). Guided mass spectrum labelling in atom probe tomography. Ultramicroscopy 159, 338–345.Google Scholar
Hall, TM, Wagner, A & Seidman, DN (1977). A computer-controlled time-of-flight atom-probe field-ion microscope for the study of defects in metals. J Phys E Sci Instrum 10, 884–891.Google Scholar
Hudson, D, Smith, GDW & Gault, B (2011). Optimisation of mass ranging for atom probe microanalysis and application to the corrosion processes in Zr alloys. Ultramicroscopy 111, 480–486.Google Scholar
Johnson, LJS, Thuvander, M, Stiller, K, Odén, M & Hultman, L (2013). Blind deconvolution of time-of-flight mass spectra from atom probe tomography. Ultramicroscopy 132, 60–64.Google Scholar
Larson, DJ, Prosa, TJ, Ulfig, RM, Geiser, BP & Kelly, ThF (2013). Local Electrode Atom Probe Tomography: A User's Guide. New York: Springer.Google Scholar
London, AJ, Haley, D & Moody, MP (2017). Single-ion deconvolution of mass peak overlaps for atom probe microscopy. Microsc Microanal 23, 300–306.Google Scholar
Marquis, EA & Hyde, JM (2010). Applications of atom-probe tomography to the characterisation of solute behaviours. Mater Sci, Eng R, Rep 69, 37–62.Google Scholar
McLachlan, G & Peel, D (2000) Wiley Series in Probability and Statistics. New York: John Wiley and Sons, Inc.Google Scholar
Müller, EW, Panitz, JA & McLane, SB (1968). The atom-probe field ion microscope. Rev Sci Instrum 39, 83–86.Google Scholar
Pareige, C, Lefebvre-Ulrikson, W, Vurpillot, F & Sauvage, X (2016). Time-of-Flight mass spectrometry and composition measurements. In Atom Probe Tomography, pp. 123–154. Elsevier http://linkinghub.elsevier.com/retrieve/pii/B978012804647000005X (Accessed July 10, 2018).Google Scholar
Polanski, A, Marczyk, M, Pietrowska, M, Widlak, P & Polanska, J (2015). Signal partitioning algorithm for highly efficient Gaussian mixture modeling in mass spectrometry Tang, H. (Ed.). PLoS ONE 10, e0134256.Google Scholar
Sarrau, J-M (1977). ‘Réalisation et Performances D'une Sonde à Atomes’. Thèse de Doctorat es Sciences Physiques, Université de Rouen.Google Scholar
Spainhour, JCG, Janech, MG, Schwacke, JH, Velez, JCQ & Ramakrishnan, V (2014). The application of Gaussian mixture models for signal quantification in MALDI-ToF mass spectrometry of peptides Bader, M. (Ed.). PLoS ONE 9, e111016.Google Scholar
Thuvander, M, Östberg, G, Ahlgren, M & Falk, LKL (2015). Atom probe tomography of a Ti–Si–Al–C–N coating grown on a cemented carbide substrate. Ultramicroscopy 159, 308–313.Google Scholar
Zelenty, J, Dahl, A, Hyde, J, Smith, GDW & Moody, MP (2017). Detecting clusters in atom probe data with Gaussian mixture models. Microsc Microanal 23, 269–278.Google Scholar