The atom-probe field ion microscope was introduced in 1967 at the 14th Field Emission Symposium held at the National Bureau of Standards (now, NIST) in Gaithersburg, Maryland. The atom-probe field ion microscope was, and remains, the only instrument capable of determining “the nature of one single atom seen on a metal surface and selected from neighboring atoms at the discretion of the observer”. The development of the atom-probe is a story of an instrument that one National Science Foundation (NSF) reviewer called “impossible because single atoms could not be detected”. It is also a story of my life with Erwin Wilhelm Müller as his graduate student in the Field Emission Laboratory at the Pennsylvania State University in the late 1960s and his strong and volatile personality, perhaps fostered by his pedigree as Gustav Hertz’s student in the Berlin of the 1930s. It is the story that has defined by scientific career.

An improved reconstruction method for atom probe tomography is presented. In this approach, the curvature of the field emitter is variable, in contrast to the conventional reconstruction technique. The information about the tip shape at different stages of the reconstruction is directly extracted from the local density of events on the detector. To this end, the detector and the tip surface are split into different segments. According to the density distribution of events observed on the detector, the size of the corresponding segment on the tip surface is calculated, yielding an emitter profile which is not necessarily spherical. The new approach is demonstrated for emitter structures with radial symmetry that contain a spherical precipitate with a substantially lower or higher evaporation field compared to the surrounding matrix. A comparison to the conventional point projection approach is made.

Current approaches to reconstruction in atom probe tomography produce results that exhibit substantial distortions throughout the analysis depth. This is largely because of the need to apply a multitude of assumptions when estimating the evolution of the tip shape, and other pseudo-empirical reconstruction factors, which vary both across the face of the tip and throughout the analysis depth. We introduce a new crystallography-mediated reconstruction to improve the spatial accuracy and dramatically reduce these in-depth variations. To achieve this, we developed a barycentric transform to directly relate atomic positions in detector space to real space. This is mediated by novel crystallographic analysis techniques, including: (1) calculating the orientation of a crystal directly from the field evaporation map, (2) tracking pole locations throughout the evaporation sequence, and (3) accounting for the evolving tip radius in a manner that removes the dependence on the geometric field factor. By improving the in-depth spatial accuracy of the atom probe reconstruction, a greater accuracy of the atomic neighborhood relationships is available. This is critical in modern materials science and engineering, where an understanding of the solid solution architecture, precipitate dispersions, and descriptions of the interfaces between phases or grains are key inputs to microstructure–property relationships.

Although atom probe tomography (APT) reconstructions do not directly influence the local elemental analysis, any structural inferences from APT volumes demand a reliable reconstruction of the point cloud. Accurate estimation of the reconstruction parameters is crucial to obtain reliable spatial scaling. In the current work, a new automated approach of calibrating atom probe reconstructions is developed using only one correlative projection electron microscopy (EM) image. We employed an algorithm that implements a 2D cross-correlation of microstructural features observed in both the APT reconstructions and the corresponding EM image. We apply this protocol to calibrate reconstructions in a Cu(In,Ga)Se2-based semiconductor and in a Co-based superalloy. This work enables us to couple chemical precision to structural information with relative ease.

We define a measure for the accuracy of tomographic reconstruction in atom probe tomography, named here the spatial error index. We demonstrate that this index can be used to compare rigorously the spatial accuracy of various different approaches to the calculation of tomographic reconstruction. This is useful, for example, to evaluate the performance of alternate tomographic reconstruction approaches, and ensures that the comparisons are independent of individual data quality or other instrumental parameters. We then introduce a new “adaptive reconstruction” formalism that uses a progression of reconstruction parameters based on a per-atom correction from the cube root of the inverse of the voltage, along with linear correction factors linked to the evaporation sequence. We apply the measure for spatial accuracy to this new reconstruction protocol.

The process of building an open source library of simulated field desorption maps for differently oriented synthetic tips of the face-centered cubic, body-centered cubic, and hexagonal-close-packed crystal structures using the open source software TAPSim is reported. Specifically, the field evaporation of a total set of 4 × 101 single-crystalline tips was simulated. Their lattices were oriented randomly to sample economically the fundamental zone of crystal orientations. Such data are intended to facilitate the interpretation of low-density zone lines and poles that are observed on detector hit maps during Atom Probe Tomography (APT) experiments. The datasets and corresponding tools have been made publicly available to the APT community in an effort to provide better access to simulated atom probe datasets. In addition, a computational performance analysis was conducted, from which recommendations are made as to which key tasks should be optimized in the future to improve the parallel efficiency of TAPSim.

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.

Atom probe tomography (APT) is a powerful technique to characterize buried three-dimensional nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure–property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the density-based spatial clustering of applications with noise algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the nonuniformity of the atomic density in the APT reconstruction itself (e.g., crystallographic poles and other field evaporation artifacts). This clustering method relies heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we utilize a well-known cluster analysis algorithm, called ordering points to identify the clustering structures, and an automatic cluster extraction algorithm to analyze clusters of varying atomic density in APT data. This approach requires only one free parameter, and other inputs can be estimated or bounded based on physical parameters, such as the lattice parameter and solute concentration. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen.

A procedure to analyze the elemental concentration distribution inside solute clusters after detection of clusters from atom probe tomography data set was proposed. We developed a code which can directly illustrate an average concentration profile inside a cluster even in the case of including various sizes of ellipsoidal clusters. The profile can be with respect to absolute distance and includes errors in each data point. The reliability of the developed code was verified by analyzing an artificial cluster model which has inhomogeneous elemental distribution. It was found that the precise estimation of cluster centroids is important and that the preferable conditions for targeting clusters are a detection efficiency of over 20%, over 30 atoms in a cluster on average, and over 100 atoms for each concentration data point.

We summarize the findings from an interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are: to bring clarity to the range of applicability of the methods; identify existing and/or needed modifications; and interpretation of past published data. Participants collected experimental data from a proton-irradiated 304 stainless steel and analyzed Cu-rich and Ni–Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfills the ideal requirements of the maximum separation method (MSM), namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast, the group analysis of the Ni–Si rich clusters by the MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among researchers. While local concentration filtering could, in principle, tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.

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.

There are many sources of random and systematic error in composition quantification by atom probe microscopy, often, however, only statistical error is reported. Significantly larger errors can occur from the misidentification of ions and overlaps or interferences of peaks in the mass spectrum. These overlaps can be solved using maximum likelihood estimation (MLE), improving the accuracy of the result, but with an unknown effect on the precision. An analytical expression for the uncertainty of the MLE solution is presented and it is demonstrated to be much more accurate than the existing methods. In one example, the commonly used error estimate was five times too small.

Literature results containing overlaps most likely underestimate composition uncertainty because of the complexity of correctly dealing with stochastic effects and error propagation. The uncertainty depends on the amount of overlapped intensity, for example being ten times worse for the CO/Fe overlap than the Cr/Fe overlap. Using the methods described here, accurate estimation of error, and the minimization of this could be achieved, providing a key milestone in quantitative atom probe. Accurate estimation of the composition uncertainty in the presence of overlaps is crucial for planning experiments and scientific interpretation of the measurements.

We introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrations on the measurement of the thickness, composition, and interfacial excess of the decorated interface are systematically analyzed and discussed.

Isoconcentration surfaces are commonly used to delineate phases in atom probe datasets. These surfaces then provide the spatial and compositional reference for proximity histograms, the number density of particles, and the volume fraction of particles within a multiphase system. This paper discusses the influence of the isoconcentration surface selection value on these quantitative outputs, using a simple oxide dispersive strengthened alloy, Fe91Ni8Zr1, as the case system. Zirconium reacted with intrinsic oxygen impurities in a consolidated ball-milled powder to precipitate nanoscale zirconia particles. The zirconia particles were identified by varying the Zr-isoconcentration values as well as by the maximum separation data mining method. The associated outputs mentioned above are elaborated upon in reference to the variation in this Zr isosurface value. Considering the dataset as a whole, a 10.5 at.% Zr isosurface provided a compositional inflection point for Zr between the particles and matrix on the proximity histogram; however, this value was unable to delineate all of the secondary oxide particles identified using the maximum separation method. Consequently, variations in the number density and volume fraction were observed as the Zr isovalue was changed to capture these particles resulting in a loss of the compositional accuracy. This highlighted the need for particle-by-particle analysis.

In this work, we demonstrate a new system for the examination of gas interactions with surfaces via atom probe tomography. This system provides capability of examining the surface and subsurface interactions of gases with a wide range of specimens, as well as a selection of input gas types. This system has been primarily developed to aid the investigation of hydrogen interactions with metallurgical samples, to better understand the phenomenon of hydrogen embrittlement. In its current form, it is able to operate at pressures from 10−6 to 1000 mbar (abs), can use a variety of gasses, and is equipped with heating and cryogenic quenching capabilities. We use this system to examine the interaction of hydrogen with Pd, as well as the interaction of water vapor and oxygen in Mg samples.

Due to the low capacity of contemporary position-sensitive detectors in atom probe tomography (APT) to detect multiple events, material analyses that exhibit high numbers of multiple events are the most subject to compositional biases. To solve this limitation, some researchers have developed statistical correction algorithms. However, those algorithms are only efficient when one is confronted with homogeneous materials having nearly the same evaporation field between elements. Therefore, dealing with more complex materials must be accompanied by a better understanding of the signal loss mechanism during APT experiments. By modeling the evaporation mechanism and the whole APT detection system, it may be possible to predict compositional and spatial biases induced by the detection system. This paper introduces a systematic study of the impact of the APT detection system on material analysis through the development of a simulation tool.

Specimen survivability is a primary concern to those who utilize atom probe tomography (APT) for materials analysis. The state-of-the-art in understanding survivability might best be described as common-sense application of basic physics principles to describe failure mechanisms. For example, APT samples are placed under near-failure mechanical-stress conditions, so reduction in the force required to initiate field evaporation must provide for higher survivability—a common sense explanation of survivability. However, the interplay of various analytical conditions (or instrumentation) and how they influence survivability (e.g., decreasing the applied evaporation field improves survivability), and which factors have more impact than others has not been studied. In this paper, we report on the systematic analysis of a material composed of a silicon-dioxide layer surrounded on two sides by silicon. In total, 261 specimens were fabricated and analyzed under a variety of conditions to correlate statistically significant survivability trends with analysis conditions and other specimen characteristics. The primary result suggests that, while applied field/force plays an obvious role in survivability for this material, the applied field alone does not predict survivability trends for silicon/silicon-dioxide interfaces. The rate at which ions are extracted from the specimen (both in terms of ions-per-pulse and pulse-frequency) has similar importance.

We propose a new method for preparing atom probe tomography specimens from nanoparticles using a fusible bismuth–indium–tin alloy as an embedding medium. Iron nanoparticles synthesized by the sodium borohydride reduction method were chosen as a model system. The as-synthesized iron nanoparticles were embedded within the fusible alloy using focused ion beam milling and ion-milled to needle-shaped atom probe specimens under cryogenic conditions. An atom probe analysis revealed boron atoms in a detected iron nanoparticle, indicating that boron from the sodium borohydride reductant was incorporated into the nanoparticle during its synthesis.

The composition of nano-sized alloy carbides formed by interphase precipitation in V–Nb and V–Ti multiple microalloyed low-carbon steels is analyzed by using three-dimensional atom probe. Carbide-forming alloying elements including V, Nb, and Ti, are simultaneously precipitated from the early stage of isothermal treatment, whose atoms are uniformly distributed in the carbide particles, even after prolonged holding. Cluster analysis by the maximum separation method, with parameters optimized using different methods, is carried out to extract alloy carbides from matrix. The composition of alloy carbides evaluated by site fraction of substitutional carbide-forming alloying elements indicates that at the early stage of their formation, Nb and Ti are more strongly enriched than V.