Maximizing information: a machine learning approach for analysis of complex nanoscale electromechanical behavior in defect-rich PZT films

Fengyuan Zhang, Kerisha N. Williams, David Edwards, Aaron B. Naden, Yulian Yao, Sabine M. Neumayer, Amit Kumar, Brian J. Rodriguez, Nazanin Bassiri-Gharb

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Article number2100552
Number of pages11
JournalSmall Methods
VolumeEarly View
Early online date22 Oct 2021
DOIs
Publication statusE-pub ahead of print - 22 Oct 2021

Keywords

  • Dimensional stacking
  • Ferroelectricity
  • Machine learning
  • On-field and Off-field piezoresponse hysteresis
  • Pb(Zr,Ti)O3 films
  • Piezoresponse force microscopy
  • Scanning Probe Microscopy

Fingerprint

Dive into the research topics of 'Maximizing information: a machine learning approach for analysis of complex nanoscale electromechanical behavior in defect-rich PZT films'. Together they form a unique fingerprint.

Cite this