Guided mass spectrum labelling in atom probe tomography

TL;DR

This paper introduces a computer-guided algorithm for mass spectrum peak labelling in atom probe tomography, reducing errors and operator variance, and improving the standardisation of the technique.

Contribution

It presents a robust, fully automated algorithm for peak identification and a ranking scheme to evaluate likelihoods, enhancing accuracy and consistency in APT data analysis.

Findings

Computer assistance maintains or improves precision in peak labelling.

Inter-operator variability is minimized with the proposed method.

Inconsistencies in manual labelling are a major source of data scatter.

Abstract

Atom probe tomography (APT) is a valuable near-atomic scale imaging technique, which yields mass spectrographic data. Experimental correctness can often pivot on the identification of peaks within a dataset, this is a manual process where subjectivity and errors can arise. The limitations of manual procedures complicate APT experiments for the operator and furthermore are a barrier to technique standardisation. In this work we explore the capabilities of computer-guided ranging to aid identification and analysis of mass spectra. We propose a fully robust algorithm for enumeration of the possible identities of detected peak positions, which assists labelling. Furthermore, a simple ranking scheme is developed to allow for evaluation of the likelihood of each possible identity being the likely assignment from the enumerated set. We demonstrate a simple, yet complete work-chain that allows for the conversion of mass-spectra to fully identified APT spectra, with the goal of minimising identification errors, and the inter-operator variance within APT experiments. This work chain is compared to current procedures via experimental trials with different APT operators, to determine the relative effectiveness and precision of the two approaches. It is found that there is little loss of precision (and occasionally gain) when participants are given computer assistance. We find that in either case, inter-operator precision for ranging varies between 0 and 2 "significant figures" (2{\sigma} confidence in the first n digits of the reported value) when reporting compositions. Intra-operator precision is weakly tested and found to vary between 1 and 3 significant figures, depending upon species composition levels. Finally it is suggested that inconsistencies in inter-operator peak labelling may be the largest source of scatter when reporting composition data in APT.