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

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

Abstract Piezoresponse force microscopy (PFM) is routinely used to probe the nanoscale electromechanical response of ferroelectric and piezoelectric materials. However, many challenges remain in the interpretation of the recovered signal. Specifically, many non‐ferroelectric contributions affect the measured response, ranging from electrostatics, to charge injection and trapping, and topographic cross‐talk. Recently, machine learning (ML) has been utilized to identify multiple contributors within complex data systems, such as PFM response. A substantial advancement in ML approaches for PFM techniques is offered by dimensional stacking, enabling encoding of physical and/or chemical correlations within the materials' response across different data dimensions spanning varying ranges. However, dimensional stacking requires appropriate scaling for each dimension (before ML analysis) to minimize undesired information loss. Here, the impact of clustering globally and locally scaled parameters in polarization switching experiments via resonant PFM (RPFM) are discussed. Specifically, dimensional stacking of scaled parameters can mask or enhance ferroelectric and non‐ferroelectric behaviors, and aid identification of various physical phenomena contributing to the measured RPFM response. This study highlights the importance of data curation for ML, and its role in identifying signal contributors to scanning probe microscopy (SPM)‐based techniques with multidimensional data, such as resonant and/or spectroscopic SPM

Domain wall contributions to the properties of piezoelectric thin films

In bulk ferroelectric ceramics, extrinsic contributions associated with motion of domain walls and phase boundaries are a significant component of the measured dielectric and piezoelectric response. In thin films, the small grain sizes, substantial residual stresses, and the high concentration of point and line defects change the relative mobility of these boundaries. One of the consequences of this is that thin films typically act as hard piezoelectrics. This paper reviews the literature in this field, emphasizing the difference between the nonlinearities observed in the dielectric and piezoelectric properties of films. The effect of ac field excitation levels, dc bias fields, temperature, and applied mechanical stress are discussed

Effects of high energy x ray and proton irradiation on lead zirconate titanate thin films' dielectric and piezoelectric response

The effects of irradiation by X rays and protons on the dielectric and piezoelectric response of highly (100)-textured polycrystalline Pb(ZrxTi1-x)O-3 (PZT) thin films have been studied. Low-field dielectric permittivity, remanent polarization, and piezoelectric d(33,f) response all degraded with exposure to radiation, for doses higher than 300 krad. At first approximation, the degradation increased at higher radiation doses, and was stronger in samples exposed to X rays, compared to the proton-irradiated ones. Nonlinear and high-field dielectric characterization suggest a radiation-induced reduction of the extrinsic contributions to the response, attributed to increased pinning of the domain walls by the radiation-induced point defects. (C) 2013 AIP Publishing LLC