Microsc. Microanal. 23, 300–306, 2017 doi:10.1017/S1431927616012782
© MICROSCOPY SOCIETY OF AMERICA 2017
Single-Ion Deconvolution of Mass Peak Overlaps for Atom Probe Microscopy
Andrew J. London,* Daniel Haley, and Michael P. Moody Department of Materials Science, University of Oxford, 16 Parks Rd, Oxford OX1 3PH, UK
Abstract: Due to the intrinsic evaporation properties of the material studied, insufficient mass-resolving power and lack of knowledge of the kinetic energy of incident ions, peaks in the atom probe mass-to-charge spectrum can overlap and result in incorrect composition measurements. Contributions to these peak overlaps can be deconvoluted globally, by simply examining adjacent peaks combined with knowledge of natural isotopic abundances. However, this strategy does not account for the fact that the relative contributions to this convoluted signal can often vary significantly in different regions of the analysis volume; e.g., across interfaces and within clusters. Some progress has been made with spatially localized deconvolution in cases where the discrete microstructural regions can be easily identified within the reconstruction, but this means no further point cloud analyses are possible. Hence, we present an ion-by-ion methodology where the identity of each ion, normally obscured by peak overlap, is resolved by examining the isotopic abundance of their immediate surroundings. The resulting peak-deconvoluted data are a point cloud and can be analyzed with any existing tools. We present two detailed case studies and discussion of the limitations of this new technique.
Key words: atom probe tomography, mass spectrum analysis, peak overlaps, peak deconvolution, atomic-scale analysis
INTRODUCTION
Atom probe tomography (APT) is a three-dimensional (3D) atomistic characterization technique which relies on time- of-flight mass spectrometry to infer elemental identities (Müller et al., 1968; Kelly&Larson, 2012). Therefore, correct identification of the peaks in the mass-to-charge state (m/z) spectrum is crucial in determining the correct mea- sured composition (Haley et al., 2015). Due to the limited mass-resolving power of APT, there is a difficulty in distin- guishing between certain ionic species. This is particularly challenging in organic (Narayan et al., 2012), oxide and ceramic materials (Kirchhofer et al., 2013; Mancini et al., 2014), which can produce complex mass spectra with molecular (Tsong, 1984) and multiply charged ions (Haydock & Kingham, 1980). However, peak overlaps can cause difficulties even with routine analysis of steels (Takahashi et al., 2011). With APT being applied to an increasing number of more complex materials (Seidman, 2007; Valley et al., 2014), there is a need to improve our analysis of the mass spectra. One area for improvement is the deconvolution of peak
overlaps. Peaks in the m/z spectrum may overlap directly (direct overlap) or a smaller peak may overlap the tail of a preceding peak (tail overlap). Tail overlap is often observed in laser pulsed mode due to the thermal transport from the tip (Vella et al., 2011) or in voltage mode due to an energy deficit (Müller & Krishnaswamy, 1974). In some cases an
*Corresponding author.
andrew.london@materials.ox.ac.uk Received July 1, 2016; accepted December 20, 2016
isobaric overlap occurs, such as 54Fe/54Cr, were the atomic masses are very similar. Direct overlaps often occur when the separation of two peaks is smaller than a value which depends on the mass-resolving power, but typically <0.1 Da. Direct overlaps can lead to incorrect measurements of the composition, and tail overlap reduces the signal- to-background ratio (contrast) of peaks. Therefore, resolving any mass peak overlaps is critical to ensure an accurate compositional measurement. In other spectral analyses, where the peak shape or
position is known, peak fitting can be used to recover the total counts in each overlapped peak (Wojdyr, 2010). However, in APT, the peak shape is dependent on the ionic species and the position may be inexact due to inaccuracies during the calibration step. The peak shape can be estimated by machine learning to fit a peak shape to an experimental spectrum (Johnson et al., 2013), but this method requires a large quantity of data to produce a reliable fit. Peak overlaps may be resolved if they vary spatially and can be revealed by multivariate statistical analysis (Parish & Miller, 2010). However, in most cases the peak area may be estimated simply by integrating the counts from a specificm/z window or range (Miller, 2000). These ranged counts for each peak and knowledge of the natural isotopic abundances can be used to estimate the contribution of each ion to the overlapped peaks. These methods are well established for calculating the composition of the whole data set, but this discards any spatial information entirely. Therefore, we seek to improve the spatial resolution of this peak deconvolution method. Kuduz et al. (2004) showed that corrections to the m/z spectrum applied to a specific volume can yield improved
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