Full information acquisition in piezoresponse force microscopy

Somnath, Suhas; Belianinov, Alex; Jesse, Stephen; Kalinin, Sergei V.

doi:10.1063/1.4938482

Title: Full information acquisition in piezoresponse force microscopy

Journal Article · Mon Dec 28 00:00:00 EST 2015 · Applied Physics Letters

DOI:https://doi.org/10.1063/1.4938482· OSTI ID:1235834

Somnath, Suhas ^[1]; Belianinov, Alex ^[1]; Jesse, Stephen ^[1]; Kalinin, Sergei V. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

The information flow from the tip-surface junction to the detector electronics during the piezoresponse force microscopy (PFM) imaging is explored using the recently developed general mode (G-mode) detection. Information-theory analysis suggests that G-mode PFM in the non-switching regime, close to the first resonance mode, contains a relatively small (100 - 150) number of components containing significant information. The first two primary components are similar to classical PFM images, suggesting that classical lock-in detection schemes provide high veracity information in this case. At the same time, a number of transient components exhibit contrast associated with surface topography, suggesting pathway to separate the two. The number of significant components increases considerably in the non-linear and switching regimes and approaching to cantilever resonances, precluding the use of classical lock-in detection and necessitating the use of band excitation or G-mode detection schemes. As a result, the future prospects of full information imaging in SPM are discussed.

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

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1235834

Alternate ID(s):: OSTI ID: 1234028

Journal Information:: Applied Physics Letters, Vol. 107, Issue 26; ISSN 0003-6951

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 26 works

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Revealing ferroelectric switching character using deep recurrent neural networks Agar, Joshua C.; Naul, Brett; Pandya, Shishir Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12750-0	journal	October 2019
Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy Borodinov, Nikolay; Neumayer, Sabine; Kalinin, Sergei V. npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0148-5	journal	February 2019
Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR Borodinov, Nikolay; Bilkey, Natasha; Foston, Marcus npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0186-z	journal	April 2019
Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography Jesse, S.; Chi, M.; Belianinov, A. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep26348	journal	May 2016
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
Molecular reorganization in bulk bottlebrush polymers: direct observation via nanoscale imaging Borodinov, Nikolay; Belianinov, Alex; Chang, Dongsook Nanoscale, Vol. 10, Issue 37 https://doi.org/10.1039/c8nr05630g	journal	January 2018
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
Tutorial: Product properties in multiferroic nanocomposites Viehland, Dwight; Li, Jie Fang; Yang, Yaodong Journal of Applied Physics, Vol. 124, Issue 6 https://doi.org/10.1063/1.5038726	journal	August 2018
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Frontiers in strain-engineered multifunctional ferroic materials Agar, Joshua C.; Pandya, Shishir; Xu, Ruijuan MRS Communications, Vol. 6, Issue 3 https://doi.org/10.1557/mrc.2016.29	journal	August 2016
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Vasudevan, Rama K.; Choudhary, Kamal; Mehta, Apurva MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.95	journal	July 2019
Rapid mapping of polarization switching through complete information acquisition Somnath, Suhas; Belianinov, Alex; Kalinin, Sergei V. Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms13290	journal	December 2016
Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data Ievlev, Anton V.; Belianinov, Alexei; Jesse, Stephen Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-17049-y	journal	December 2017

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