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The Maximum Separation Cluster Analysis Algorithm for Atom-Probe Tomography: Parameter Determination and Accuracy

Published online by Cambridge University Press:  20 October 2014

Eric Aimé Jägle ,
Pyuck-Pa Choi  and
Dierk Raabe
Eric Aimé Jägle*
Affiliation:
Metal Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
Pyuck-Pa Choi
Affiliation:
Metal Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
Dierk Raabe
Affiliation:
Metal Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
*
*Corresponding author. e.jaegle@mpie.de
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Abstract

Atom-probe tomography is a materials characterization method ideally suited for the investigation of clustering and precipitation phenomena. To distinguish the clusters from the surrounding matrix, the maximum separation algorithm is widely employed. However, the results of the cluster analysis strongly depend on the parameters used in the algorithm and hence, a wrong choice of parameters leads to erroneous results, e.g., for the cluster number density, concentration, and size. Here, a new method to determine the optimum value of the parameter dmax is proposed, which relies only on information contained in the measured atom-probe data set. Atom-probe simulations are employed to verify the method and to determine the sensitivity of the maximum separation algorithm to other input parameters. In addition, simulations are used to assess the accuracy of cluster analysis in the presence of trajectory aberrations caused by the local magnification effect. In the case of Cu-rich precipitates (Cu concentration 40–60 at% and radius 0.25–1.0 nm) in a bcc Fe–Si–Cu matrix, it is shown that the error in concentration is below 10 at% and the error in radius is <0.15 nm for all simulated conditions, provided that the correct value for dmax, as determined with the newly proposed method, is employed.

Keywords

atom-probe tomographycluster analysisfield evaporation simulationclusteringelectrical steel
Type
Technology and Software Development
Information
Microscopy and Microanalysis , Volume 20 , Issue 6 , December 2014 , pp. 1662 - 1671
Copyright
© Microscopy Society of America 2014 

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References

Bas, P., Bostel, A., Deconihout, B. & Blavette, D. (1995). A general protocol for the reconstruction of 3D atom probe data. Appl Surf Sci 87–88, 298–304.CrossRefGoogle Scholar
Blavette, D., Vurpillot, F., Pareige, P. & Menand, A. (2001). A model accounting for spatial overlaps in 3D atom-probe microscopy. Ultramicroscopy 89, 145–153.CrossRefGoogle Scholar
Fine, M., Liu, J. & Asta, M. (2007). An unsolved mystery: The composition of bcc Cu alloy precipitates in bcc Fe and steels. Mater Sci Eng 463, 271–274.CrossRefGoogle Scholar
Gault, B., Moody, M.P., Cairney, J.M. & Ringer, S.P. (2012). Atom Probe Microscopy, vol. 160, Springer Series in Materials Science. New York, NY: Springer Verlag.CrossRefGoogle Scholar
Heinrich, A., Al-Kassab, T. & Kirchheim, R. (2003). Investigation of the early stages of decomposition of Cu 0.7at. % Fe with the tomographic atom probe. Mater Sci Eng 353, 92–98.CrossRefGoogle Scholar
Hyde, J.M. & English, C.A. (2000). An analysis of the structure of irradiation induced Cu-enriched clusters in low and high nickel welds. Mater Res Soc Proc 650(R6.6), 6–12.CrossRefGoogle Scholar
Hyde, J.M., Marquis, E.A., Wilford, K.B. & Williams, T.J. (2011). A sensitivity analysis of the maximum separation method for the characterisation of solute clusters. Ultramicroscopy 111, 440–447.Google Scholar
Kolli, R.P. & Seidman, D.N. (2007). Comparison of compositional and morphological atom-probe tomography analyses for a multicomponent Fe-Cu steel. Microsc Microanal 13, 272–284.Google Scholar
Lefebvre, W., Philippe, T. & Vurpillot, F. (2011). Application of Delaunay tessellation for the characterization of solute-rich clusters in atom probe tomography. Ultramicroscopy 111, 200–206.CrossRefGoogle ScholarPubMed
Marceau, R.K.W., Sha, G., Ferragut, R., Dupasquier, A. & Ringer, S.P. (2010 a). Solute clustering in Al-Cu-Mg alloys during the early stages of elevated temperature ageing. Acta Mater 58, 4923–4939.CrossRefGoogle Scholar
Marceau, R.K.W., Sha, G., Lumley, R.N. & Ringer, S.P. (2010 b). Evolution of solute clustering in Al-Cu-Mg alloys during secondary ageing. Acta Mater 58, 1795–1805.Google Scholar
Marquis, E.A. & Hyde, J.M. (2010). Applications of atom-probe tomography to the characterisation of solute behaviours. Mater Sci Eng 69, 37–62.Google Scholar
Marquis, E.A. & Vurpillot, F. (2008). Chromatic aberrations in the field evaporation behavior of small precipitates. Microsc Microanal 14, 561–570.Google Scholar
Miller, M.K., Cerezo, A., Hetherrington, M.G. & Smith, G.D.W. (1996). Atom Probe Field Ion Microscopy . Oxford, UK: Oxford University Press.Google Scholar
Miller, M.K., Russell, K.F. & Hoelzer, D.T. (2006). Characterization of precipitates in MA/ODS ferritic alloys. J Nucl Mater 351, 261–268.Google Scholar
Morley, A., Sha, G., Hirosawa, S., Cerezo, A. & Smith, G.D.W. (2009). Determining the composition of small features in atom probe: bcc Cu-rich precipitates in an Fe-rich matrix. Ultramicroscopy 109, 535–540.CrossRefGoogle Scholar
Oberdorfer, C., Eich, S.M. & Schmitz, G. (2013). A full-scale simulation approach for atom probe tomography. Ultramicroscopy 128, 55–67.Google Scholar
Oberdorfer, C. & Schmitz, G. (2011 a). On the field evaporation behavior of dielectric materials in three-dimensional atom probe: A numeric simulation. Microsc Microanal 17, 15–25.Google Scholar
Oberdorfer, C. & Schmitz, G. (2011 b). TAPSim simulation package. http://www.wwu.de/Physik.MP/Schmitz/en/tapsim/index.html (retrieved October 9, 2014).Google Scholar
Oh, J., Ohkubo, T., Mukai, T. & Hono, K. (2005). TEM and 3DAP characterization of an age-hardened Mg-Ca-Zn alloy. Scripta Mater 53, 675–679.CrossRefGoogle Scholar
Panciera, F., Baudot, S., Hoummada, K., Gregoire, M., Juhel, M. & Mangelinck, D. (2012). Three-dimensional distribution of Al in high-k metal gate: Impact on transistor voltage threshold. Appl Phys Lett 100, 201909.CrossRefGoogle Scholar
Philippe, T., Cojocaru-Mirédin, O., Duguay, S. & Blavette, D. (2010). Clustering and nearest neighbour distances in atom probe tomography: The influence of the interfaces. J Microsc 239, 72–77.CrossRefGoogle ScholarPubMed
Schober, M., Eidenberger, E., Leitner, H., Staron, P., Reith, D. & Podloucky, R. (2010). A critical consideration of magnetism and composition of (bcc) Cu precipitates in (bcc) Fe. Appl Phys 99, 697–704.CrossRefGoogle Scholar
Schober, M., Eidenberger, E., Staron, P. & Leitner, H. (2011). Critical consideration of precipitate analysis of Fe 1 at.% Cu using atom probe and small-angle neutron scattering. Microsc Microanal 17, 26–33.Google Scholar
Schwarz, T., Cojocaru-Mirédin, O., Choi, P., Mousel, M., Redinger, A., Siebentritt, S. & Raabe, D. (2013). Atom probe study of Cu2ZnSnSe4 thin-films prepared by co-evaporation and post-deposition annealing. Appl Phys Lett 102, 042101.Google Scholar
Serizawa, A. & Miller, M.K. (2013). Radius dependence of solute concentration estimates of simulated ultrafine precipitates. Microsc Res Tech 76, 1196–1203.Google Scholar
Stephenson, L.T. (2009). Cluster analysis under microscope: quantitative techniques for atom probe analysis of solute clustering in multi-component materials. PhD Thesis, University of Sydney, Sydney.Google Scholar
Stephenson, L.T., Moody, M.P., Liddicoat, P.V. & Ringer, S.P. (2007). New techniques for the analysis of fine-scaled clustering phenomena within atom probe tomography (APT) data. Microsc Microanal 13, 448–463.Google Scholar
Vaumousse, D., Cerezo, A. & Warren, P.J. (2003). A procedure for quantification of precipitate microstructures from three-dimensional atom probe data. Ultramicroscopy 95, 215–221.CrossRefGoogle ScholarPubMed
Vurpillot, F., Bostel, A. & Blavette, D. (2000). Trajectory overlaps and local magnification in three-dimensional atom probe. Appl Phys Lett 76, 3127.Google Scholar
Vurpillot, F., De Geuser, F., Da Costa, G. & Blavette, D. (2004). Application of Fourier transform and autocorrelation to cluster identification in the three-dimensional atom probe. J Microsc 216, 234–240.Google Scholar
Williams, C.A., Haley, D., Marquis, E.A., Smith, G.D.W. & Moody, M.P. (2013). Defining clusters in APT reconstructions of ODS steels. Ultramicroscopy 132, 271–278.Google Scholar