Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect‐Rich PZT Films. Issue 12 (22nd October 2021)

• Record Type: Journal Article
• Authors: Zhang, Fengyuan; Williams, Kerisha N.; Edwards, David; Naden, Aaron B.; Yao, Yulian; Neumayer, Sabine M.; Kumar, Amit; Rodriguez, Brian J.; Bassiri‐Gharb, Nazanin
• Is Part Of: Small methods. Volume 5:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-22
• Subjects: dimensional stacking -- ferroelectricity -- machine learning -- On‐field and Off‐field piezoresponse hysteresis -- Pb(Zr, Ti)O 3 films -- piezoresponse force microscopy -- Scanning Probe Microscopy; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.202100552
• Languages: English
• ISSNs: 2366-9608

Abstract

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. Abstract : Maximizing information density, compared to signal‐to‐noise, enables quantitative separation of piezoelectric and non‐piezoelectric electromechanical contributors in piezoresponse force microscopy, even in weakly piezoelectrically active materials. The inclusion of both on‐ and off‐field piezoresponse as well as off‐field resonance frequency via dimensional stacking facilitates the separation of ferroelectric switching, electrostatic effects, and complex non‐ferroelectric contributors in a defective PZT thin film.