Direct processing of PbZr0.53Ti0.47O3 films on glass and polymeric substrates

This work was supported by the U.S. National Science Foundation under grant No. CMMI-1537262, Science Foundation Ireland (SFI) under the US-Ireland R&D Partnership Programme Grant Number SFI/14/US/I3113, the China Scholarship Council, and the Department of Education and Learning NI through grant USI-082.This work reports on direct crystallization of PbZr0.53Ti0.47O3 (PZT) thin films on glass and polymeric substrates, using pulsed thermal processing (PTP). Specifically, xenon flash lamps deliver pulses of high intensity, short duration, broadband light to the surface of a chemical solution deposited thin film, resulting in the crystallization of the film. Structural analysis by X-ray diffraction (XRD) and transmission electron microscopy show the existence of perovskite structure in nano-sized grains (≤5 nm). Local functional analysis by band excitation piezoelectric spectroscopy and electrostatic force microscopy confirm the presence of a ferroelectric phase and retention of voltage-written polarization for multiple days. Based on structural and functional analyses, strategies are discussed for optimization of pulse voltage and duration for the realization of crystalline ferroelectric thin films. For ∼200 nm-thick PZT films on glass substrates, 500 μs-long pulses were required for crystallization, starting with 100 pulses at 350 V, 10 or 25 pulses at 400 V and in general lower number of pulses at higher voltages (resulting in higher radiant energy). Overall power densities of >6.4 kW/cm2 were needed for appearance of peaks corresponding to the perovskite phase in the XRD. Films on glass processed at 350–400 V had a higher degree of 111-oriented perovskite grains. Higher applied radiant energy (through increased pulse voltage or count) resulted in more random and/or partially 001-oriented films. For ∼1 μm-thick PZT films on polymeric substrates, 10 to 25 250 μs-long pulses at voltages ranging between 200 to 250 V, corresponding to power densities of ∼2.8 kW/cm2, were optimal for maximized perovskite phase crystallization, while avoiding substrate damage.PostprintPeer reviewe

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

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking

Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials\u27 functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials\u27 behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction analysis, through dimensional stacking. Compared to traditional, uncorrelated techniques, this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters, through stacking of data along specific dimensions as required by physical or chemical correlations. The proposed method is applied to the nanoscale electromechanical relaxation response in (1 − x)PMN-xPT solid solutions, enabling a direct comparison of electric field- and chemical composition-dependent contributors. A poling-like, and a relaxation-like behavior with a domain glass state are identified, and their evolution is tracked across the phase diagram. The proposed dimensional stacking technique, guided by the knowledge of the underlying physics of correlated systems, is valid for the analysis of any multidimensional dataset, opening a spectrum of possibilities for multidisciplinary use