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Title: Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

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 in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1608212
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; imaging techniques; scanning probe microscopy

Citation Formats

Ziatdinov, Maxim, Kim, Dohyung, Neumayer, Sabine M., Vasudevan, Rama K., Collins, Liam, Jesse, Stephen, Ahmadi, Mahshid, and Kalinin, Sergei V. Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling. United States: N. p., 2020. Web. doi:10.1038/s41524-020-0289-6.
Ziatdinov, Maxim, Kim, Dohyung, Neumayer, Sabine M., Vasudevan, Rama K., Collins, Liam, Jesse, Stephen, Ahmadi, Mahshid, & Kalinin, Sergei V. Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling. United States. doi:https://doi.org/10.1038/s41524-020-0289-6
Ziatdinov, Maxim, Kim, Dohyung, Neumayer, Sabine M., Vasudevan, Rama K., Collins, Liam, Jesse, Stephen, Ahmadi, Mahshid, and Kalinin, Sergei V. Thu . "Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling". United States. doi:https://doi.org/10.1038/s41524-020-0289-6. https://www.osti.gov/servlets/purl/1608212.
@article{osti_1608212,
title = {Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling},
author = {Ziatdinov, Maxim and Kim, Dohyung and Neumayer, Sabine M. and Vasudevan, Rama K. and Collins, Liam and Jesse, Stephen and Ahmadi, Mahshid and Kalinin, Sergei V.},
abstractNote = {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 in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.},
doi = {10.1038/s41524-020-0289-6},
journal = {npj Computational Materials},
number = 1,
volume = 6,
place = {United States},
year = {2020},
month = {3}
}

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