Learning Atom Probe Tomography time-of-flight peaks for mass-to-charge ratio spectrometry

Published

April 2, 2022

Author(s)

Kevin J. Coakley, Norman A. Sanford

Abstract

In laser-assisted atom probe tomography, an important goal is to reconstruct the mass-to-charge ratio, (m/z), spectrum due to various ion species. In general, the probability mass function (pmf) associated with the time-of- flight (TOF) spectrum produced by each ion species is unknown and varies from species-to-species. Moreover, measuring pmfs for distinct ion species in calibration experiments is not practical. Here, we present a mixture model method to determine TOF pmfs that can vary from peak-to-peak. In this approach, we determine weights of candidate pmfs with a maximum likelihood method. In a proof-of-principle study, we apply our method to a TOF spectrum acquired from a silicon sample and determine intensity estimates of singly charged isotopes of silicon.

Citation

Ultramicroscopy

Volume

237

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.ultramic.2022.113521

Local Download

Keywords

Atom Probe Tomography, Cross-validation, Expectation Maximization algorithm, Machine learning, Maximum likelihood, Mixture model, Silicon isotopes

Citation

Coakley, K. and Sanford, N. (2022), Learning Atom Probe Tomography time-of-flight peaks for mass-to-charge ratio spectrometry, Ultramicroscopy, [online], https://doi.org/10.1016/j.ultramic.2022.113521, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933749 (Accessed October 13, 2025)