A computational geometry framework for the optimisation of atom probe reconstructions.

Area of Science:

• Materials Science
• Computational Geometry
• Data Analysis

Background:

• Atom probe tomography (APT) is a powerful technique for 3D material characterization at the atomic scale.
• Reconstruction of APT data, especially at coarser scales (>10nm), can suffer from trajectory aberrations and shape deviations.
• Improving reconstruction accuracy is crucial for reliable nanoscale material analysis.

Purpose of the Study:

• To present novel pathways for improving atom probe data reconstruction on a coarse scale (>10nm).
• To introduce computational geometry-based methods for iterative refinement of APT reconstructions.
• To address and compensate for ion trajectory aberrations and overall shape deviations in APT datasets.

Main Methods:

• Application of computational geometry principles to APT data.
• Iterative adjustment of reconstructions to satisfy known shape criteria.
• Utilizing barycentric coordinate transforms for implicit approximation of reconstructions.
• Developing a method to obtain a dataset hull in detector and reconstruction space.
• Employing a 'master curve' for compensating ion trajectory aberrations.

Main Results:

• Demonstrated improvement in APT reconstruction accuracy at the >10nm scale.
• Successful compensation of trajectory aberrations using computational geometry.
• Iterative refinement of a grain boundary dataset reconstruction.
• Enhanced data utilization through dataset hull generation.
• Correction of overall shape deviations in APT data.

Conclusions:

• Computational geometry offers effective pathways for enhancing APT data reconstruction quality.
• The presented methods improve accuracy and data utilization in nanoscale material characterization.
• These techniques provide a robust framework for correcting aberrations and shape deviations in APT datasets.