Compensating Local Magnifications in Atom Probe Tomography for Accurate Analysis of Nano-Sized Precipitates.

Local magnification artifacts in atom probe tomography (APT) caused by multiphase materials with heterogeneous evaporation behavior are a well-known problem. In particular, the analysis of the exact size, shape, and composition of small precipitates is, therefore, not trivial. We performed numerical simulations of APT measurements to predict the reconstructed morphology of precipitates with contrasting evaporation thresholds. Based on a statistical approach that avoids coarse graining, the simulated data are evaluated to develop a model for the calculation of the original size of the precipitates. The model is tested on experimental APT data of precipitates with a higher and lower evaporation field in a ferritic alloy. Accurate sizes, proven by a complementary investigation by transmission electron microscopy, are obtained. We show further, how the size information can be used to obtain compositional information of the smallest precipitates and present a new methodology to determine a correct in-depth scaling of the APT reconstruction in case no complementary geometric information about the specimen exists or if no lattice planes are visible in the reconstruction.

Hierarchical density-based cluster analysis framework for atom probe tomography data.

Atom probe tomography (APT) has enabled the direct visualization of solute clusters. However one of the main analysis methods used by the APT community, i.e. the maximum separation method, often suffers from subjective parametric selection and limited applicability. To address the need for more robust and versatile analysis tools, a framework based on hierarchical density-based cluster analysis is implemented. Cluster analysis begins with the HDBSCAN algorithm to conservatively segment the datasets into regions containing clusters and a matrix or noise region. The stability of each cluster and the probability that an atom belongs to a cluster are quantified. Each clustered region is further analyzed by the DeBaCl algorithm to separate and refine clusters present in the sub-volumes. Finally, the k-nearest neighbor algorithm may be used to re-assign matrix atoms to clusters, based on their probability values. Four mandatory parameters are required for this cluster analysis approach. However, the selection of an appropriate value for only one of these parameters, i.e. a rough estimate of the minimum cluster size, is essential. The improved performance of the method was evaluated by analyzing four synthetic APT datasets and comparing the outcome with the commonly-used maximum separation method. Codes and data are made available through GitHub.