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

F.Z. and B.J.R. gratefully acknowledge support from the China Scholarship Council and Science Foundation Ireland (US-Ireland R&D Partnership Programme (SFI/14/US/I3113) and Career Development Award (SFI/17/CDA/4637) with support from the Sustainable Energy Authority of Ireland). A.N. gratefully acknowledges support from the Engineering and Physics Sciences Research Council (EPSRC) through grants EP/R023751/1 and EP/L017008/1. A.K. gratefully acknowledges support from Department of Education and Learning NI through grant USI-082 and Engineering and Physical Sciences Research Council via grant EP/S037179/1. K.W., Y.Y., and N.B.G. gratefully acknowledge support from the US National Science Foundation through grant CMMI-1537262 and DMR-1255379. K.W. and N.B.G. also acknowledge support through DMR-2026976. This publication has emanated from research supported in part by a grant from Science Foundation Ireland under Grant numbers SFI/14/US/I3113 and SFI/17/CDA/4637.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.Publisher PDFPeer reviewe