Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling

TL;DR

This paper demonstrates how Gaussian Process modeling can effectively reconstruct and analyze hyperspectral scanning probe microscopy data, enabling faster imaging and automated experimentation with sparse sampling.

Contribution

It introduces a Gaussian Process approach for hyperspectral SPM data reconstruction, showing efficient data usage and potential for automation in scanning probe microscopy.

Findings

GP methods determine characteristic length scales in spatial and frequency domains.

Approximately 30% of data suffices for high-quality reconstruction.

GP can facilitate automated, non-rectangular scanning in SPM.

Abstract

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled, with ~30% of the original data set sufficient for high-quality reconstruction, potentially enabling the faster BE imaging. Finally, we discuss how the GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans. The full code for GP regression applied to hyperspectral data is available at https://git.io/JePGr.