Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

Balke, Nina; Kalinin, Sergei V.; Jesse, Stephen; Collins, Liam; Belianinov, Alex; Somnath, Suhas

doi:10.1038/srep30557

Title: Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

Journal Article · Fri Aug 12 00:00:00 EDT 2016 · Scientific Reports

DOI:https://doi.org/10.1038/srep30557· OSTI ID:1295108

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

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

Kelvin probe force microscopy (KPFM) has provided deep insights into the role local electronic, ionic and electrochemical processes play on the global functionality of materials and devices, even down to the atomic scale. Conventional KPFM utilizes heterodyne detection and bias feedback to measure the contact potential difference (CPD) between tip and sample. This measurement paradigm, however, permits only partial recovery of the information encoded in bias- and time-dependent electrostatic interactions between the tip and sample and effectively down-samples the cantilever response to a single measurement of CPD per pixel. This level of detail is insufficient for electroactive materials, devices, or solid-liquid interfaces, where non-linear dielectrics are present or spurious electrostatic events are possible. Here, we simulate and experimentally validate a novel approach for spatially resolved KPFM capable of a full information transfer of the dynamic electric processes occurring between tip and sample. General acquisition mode, or G-Mode, adopts a big data approach utilising high speed detection, compression, and storage of the raw cantilever deflection signal in its entirety at high sampling rates (> 4 MHz), providing a permanent record of the tip trajectory. We develop a range of methodologies for analysing the resultant large multidimensional datasets involving classical, physics-based and information-based approaches. Physics-based analysis of G-Mode KPFM data recovers the parabolic bias dependence of the electrostatic force for each cycle of the excitation voltage, leading to a multidimensional dataset containing spatial and temporal dependence of the CPD and capacitance channels. We use multivariate statistical methods to reduce data volume and separate the complex multidimensional data sets into statistically significant components that can then be mapped onto separate physical mechanisms. Overall, G-Mode KPFM offers a new paradigm to study dynamic electric phenomena in electroactive interfaces as well as offer a promising approach to extend KPFM to solid-liquid interfaces.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1295108

Journal Information:: Scientific Reports, Vol. 6; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 42 works

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