Abstract
Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the Off-field piezoresponse, compared to On-field piezoresponse, based on the latter's increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating both Off- and On-field piezoresponse response as well as Off-field resonance frequency to maximize information, switching piezoresponse in a defect-rich Pb(Zr,Ti)O3 thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during On-field measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response.
Highlights
Scanning Probe Microscopy (SPM) based techniques employ In PFM an ac electric field is applied to the sample, through a cantilever to locally probe a materials surface and allow the conducting probe tip contact to the sample surface, for the acquisition of multiple observable quantities
We investigate and classify the local band excitation piezoelectric spectroscopy (BEPS) switching response in a defect-rich Pb(Zr0.53,Ti0.47)O3 thin film, fabricated via chemical solution deposition and a novel pulsed thermal processing (PTP) crystallization technique on a glass substrate.[29]
We employed a combination of electron microscopy, atomic force microscopy as well as band excitation piezoelectric spectroscopy with Machine learning (ML) analysis, to characterize the microstructural and functional properties of a defect-rich PZT sample processed by pulsed-thermal-processing
Summary
In PFM an ac electric field is applied to the sample, through a cantilever to locally probe a materials surface and allow the conducting probe tip contact to the sample surface, for the acquisition of multiple observable quantities J. Rodriguez Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield, Dublin D04 V1W8, Ireland. Bassiri-Gharb School of Materials Science and Engineering Georgia Institute of Technology Atlanta, GA 30332-0405, USA. Naden University of St Andrews School of Chemistry Purdie Building, St Andrews, Fife KY16 9ST, United Kingdom. A. Kumar Centre for Nanostructured Media School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN, United Kingdom
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
