Refining spatial distribution maps for atom probe tomography via data dimensionality reduction methods

A mathematical framework based on singular value decomposition is used to analyze the covariance among interatomic frequency distributions in spatial distribution maps (SDMs). Using this approach, singular vectors that capture the covariance within the SDM data are obtained. The structurally relevant singular vectors (SRSVs) are identified. Using the SRSVs, we extract information from z-SDMs that not only captures the offset between the atomic planes but also captures the covariance in the atomic structure among the neighborhood atomic planes. These refined z-SDMs classify the Δ(Δz) slices in the SDMs into structurally relevant information, noise, and aberrations. The SRSVs are used to construct refined xy-SDMs that provide enhanced structural information for three-dimensional atom probe tomography.