Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

Ziatdinov, Maxim; Kim, Dohyung; Neumayer, Sabine M.; Vasudevan, Rama K.; Collins, Liam; Jesse, Stephen; Ahmadi, Mahshid; Kalinin, Sergei V.

doi:10.1038/s41524-020-0289-6

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

Journal Article · 19 March 2020 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-020-0289-6 · OSTI ID:1608212

^[1]; Kim, Dohyung ^[2];

^[1];

^[1]; Ahmadi, Mahshid ^[2];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1608212

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 6; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (36)

Model-based extraction of material properties in multifrequency atomic force microscopy Forchheimer, Daniel; Platz, Daniel; Tholén, Erik A. Physical Review B, Vol. 85, Issue 19, Article No. 195449 https://doi.org/10.1103/physrevb.85.195449	journal	May 2012
Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond–Random-Field Ising Model Ovchinnikov, O. S.; Jesse, S.; Bintacchit, P. Physical Review Letters, Vol. 103, Issue 15 https://doi.org/10.1103/physrevlett.103.157203	journal	October 2009
Modification and detection of domains on ferroelectric PZT films by scanning force microscopy Franke, K.; Besold, J.; Haessler, W. Surface Science, Vol. 302, Issue 1-2 https://doi.org/10.1016/0039-6028(94)91089-8	journal	January 1994
Jags Kruschke, John K. Doing Bayesian Data Analysis https://doi.org/10.1016/B978-0-12-405888-0.00008-8	book	November 2014
Dynamic atomic force microscopy methods García, R. Surface Science Reports, Vol. 47, Issue 6-8 https://doi.org/10.1016/S0167-5729(02)00077-8	journal	September 2002
Breaking the Time Barrier in Kelvin Probe Force Microscopy: Fast Free Force Reconstruction Using the G-Mode Platform Collins, Liam; Ahmadi, Mahshid; Wu, Ting ACS Nano, Vol. 11, Issue 9 https://doi.org/10.1021/acsnano.7b02114	journal	August 2017
Decoupling Electrochemical Reaction and Diffusion Processes in Ionically-Conductive Solids on the Nanometer Scale Balke, Nina; Jesse, Stephen; Kim, Yoongu ACS Nano, Vol. 4, Issue 12 https://doi.org/10.1021/nn101502x	journal	November 2010
Complete information acquisition in dynamic force microscopy Belianinov, Alexei; Kalinin, Sergei V.; Jesse, Stephen Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms7550	journal	March 2015
How the doors to the nanoworld were opened Gerber, Christoph; Lang, Hans Peter Nature Nanotechnology, Vol. 1, Issue 1 https://doi.org/10.1038/nnano.2006.70	journal	October 2006
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design Lookman, Turab; Balachandran, Prasanna V.; Xue, Dezhen npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0153-8	journal	February 2019
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva Scientific Reports, Vol. 4, Issue 1 https://doi.org/10.1038/srep06367	journal	September 2014
Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space Collins, Liam; Belianinov, Alex; Somnath, Suhas Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep30557	journal	August 2016
Kelvin probe force microscopy Nonnenmacher, M.; O’Boyle, M. P.; Wickramasinghe, H. K. Applied Physics Letters, Vol. 58, Issue 25 https://doi.org/10.1063/1.105227	journal	June 1991
Acoustic microscopy by atomic force microscopy Rabe, U.; Arnold, W. Applied Physics Letters, Vol. 64, Issue 12 https://doi.org/10.1063/1.111869	journal	March 1994
Switching spectroscopy piezoresponse force microscopy of ferroelectric materials Jesse, Stephen; Baddorf, Arthur P.; Kalinin, Sergei V. Applied Physics Letters, Vol. 88, Issue 6 https://doi.org/10.1063/1.2172216	journal	February 2006
Intermodulation atomic force microscopy Platz, Daniel; Tholén, Erik A.; Pesen, Devrim Applied Physics Letters, Vol. 92, Issue 15 https://doi.org/10.1063/1.2909569	journal	April 2008
Rapid multidimensional data acquisition in scanning probe microscopy applied to local polarization dynamics and voltage dependent contact mechanics Jesse, Stephen; Maksymovych, Peter; Kalinin, Sergei V. Applied Physics Letters, Vol. 93, Issue 11 https://doi.org/10.1063/1.2980031	journal	September 2008
Full information acquisition in piezoresponse force microscopy Somnath, Suhas; Belianinov, Alexei; Kalinin, Sergei V. Applied Physics Letters, Vol. 107, Issue 26 https://doi.org/10.1063/1.4938482	journal	December 2015
G-mode magnetic force microscopy: Separating magnetic and electrostatic interactions using big data analytics Collins, Liam; Belianinov, Alex; Proksch, Roger Applied Physics Letters, Vol. 108, Issue 19 https://doi.org/10.1063/1.4948601	journal	May 2016
Magnetic imaging by ‘‘force microscopy’’ with 1000 Å resolution Martin, Y.; Wickramasinghe, H. K. Applied Physics Letters, Vol. 50, Issue 20 https://doi.org/10.1063/1.97800	journal	May 1987
Band excitation in scanning probe microscopy: sines of change Jesse, Stephen; Kalinin, Sergei V. Journal of Physics D: Applied Physics, Vol. 44, Issue 46 https://doi.org/10.1088/0022-3727/44/46/464006	journal	November 2011
The band excitation method in scanning probe microscopy for rapid mapping of energy dissipation on the nanoscale Jesse, Stephen; Kalinin, Sergei V.; Proksch, Roger Nanotechnology, Vol. 18, Issue 43 https://doi.org/10.1088/0957-4484/18/43/435503	journal	September 2007
Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L. Nanotechnology, Vol. 20, Issue 40 https://doi.org/10.1088/0957-4484/20/40/405708	journal	September 2009
Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy Jesse, Stephen; Kalinin, Sergei V. Nanotechnology, Vol. 20, Issue 8 https://doi.org/10.1088/0957-4484/20/8/085714	journal	February 2009
Spatially resolved mapping of disorder type and distribution in random systems using artificial neural network recognition Kumar, A.; Ovchinnikov, O.; Guo, S. Physical Review B, Vol. 84, Issue 2 https://doi.org/10.1103/PhysRevB.84.024203	journal	July 2011
Model-based extraction of material properties in multifrequency atomic force microscopy Forchheimer, Daniel; Platz, Daniel; Tholén, Erik A. Physical Review B, Vol. 85, Issue 19, Article No. 195449 https://doi.org/10.1103/PhysRevB.85.195449	journal	May 2012
Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond–Random-Field Ising Model Ovchinnikov, O. S.; Jesse, S.; Bintacchit, P. Physical Review Letters, Vol. 103, Issue 15 https://doi.org/10.1103/PhysRevLett.103.157203	journal	October 2009
Atomic Force Microscope Binnig, G.; Quate, C. F.; Gerber, Ch. Physical Review Letters, Vol. 56, Issue 9 https://doi.org/10.1103/PhysRevLett.56.930	journal	March 1986
Nanoscale Visualization and Control of Ferroelectric Domains by Atomic Force Microscopy Kolosov, Oleg; Gruverman, Alexei; Hatano, Jun Physical Review Letters, Vol. 74, Issue 21 https://doi.org/10.1103/PhysRevLett.74.4309	journal	May 1995
DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing Su, Yuanchao; Li, Jun; Plaza, Antonio IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Issue 7 https://doi.org/10.1109/TGRS.2018.2890633	journal	July 2019
Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform Kannan, R.; Ievlev, A. V.; Laanait, N. Advanced Structural and Chemical Imaging, Vol. 4, Issue 1 https://doi.org/10.1186/s40679-018-0055-8	journal	April 2018
Full information acquisition in piezoresponse force microscopy Somnath, Suhas; Belianinov, Alexei; Kalinin, Sergei Oak Ridge Leadership Computing Facility; Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) https://doi.org/10.13139/olcf/1464456	dataset	January 2018
Spectroscopic imaging in piezoresponse force microscopy: New opportunities for studying polarization dynamics in ferroelectrics and multiferroics Vasudevan, R. K.; Jesse, S.; Kim, Y. MRS Communications, Vol. 2, Issue 3 https://doi.org/10.1557/mrc.2012.15	journal	July 2012
Active-learning and materials design: the example of high glass transition temperature polymers Kim, Chiho; Chandrasekaran, Anand; Jha, Anurag MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.78	journal	June 2019
BE PFM of BiFeO3 film Maxim Ziatdinov, Dohyung Kim Zenodo https://doi.org/10.5281/zenodo.3667000	dataset	January 2020
Gaussian Processes for Machine Learning Rasmussen, Carl Edward; Williams, Christopher K. I. The MIT Press https://doi.org/10.7551/mitpress/3206.001.0001	book	January 2005

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Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials Ziatdinov, Maxim; Kim, Dohyung; Neumayer, Sabine arXiv https://doi.org/10.48550/arxiv.2002.03591	text	January 2020

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