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Interfaces between functional ceramics, such as Pb(Zr0.5Ti0.5)O3 or PZT, and metal electrodes, such as Pt, are important for many devices. Maintaining an interface that is free of secondary phases is necessary for the efficient transfer of electrons and device function. However, there are instances where unstable transient phases form at the interface due to atomic diffusion, such as Pt3Pb. Here, we investigate the migration barriers for the diffusion of Pb across the PZT/Pt and PZT/Pt3Pb interfaces using density functional theory ( DFT) and the climbing image nudge elastic band (c- NEB) method. Our calculation models take into account the influence of atmospheric conditions on Pb diffusion through the preferential stabilization of defects near the interface as a result of changes to the Pb and O chemical potentials. In addition, the PZT structures that are stable above and below the Curie temperature are considered. The migration barriers are predicted to be strongly dependent on atmospheric conditions and the phase of the PZT, tetragonal or cubic. In particular, an inversion of the Pb diffusion direction at the PZT/Pt interface is predicted to take place as the oxygen partial pressure increases. This prediction is confirmed by experimental in situ X-ray diffraction measurements of a PZT/Pt interface. [ABSTRACT FROM AUTHOR]

Domain walls in ferroelectric materials have tantalizing potential in disruptive memory and reconfigurable nanoelectronics technologies. Here, a ferroelectric domain wall switch with three distinct addressable resistance states is demonstrated. The device operation hinges on fully controllable and reversible conformational changes of the domain wall. As validated by atomistic simulations consistent with the experiments, using electric field, the shape—and hence the charge state—of the domain wall and ultimately its conduction are altered. Sequential nanoscale transitions of the walls are visualized directly using stroboscopic‐piezoresponse force microscopy and Kelvin probe microscopy. Anisotropic head‐to‐head domain wall injection, stabilized by the majority carrier type of the ferroelectric, BiFeO3, is identified as the key factor that enables conformational control. [ABSTRACT FROM AUTHOR]

Defects have a significant influence on the polarization and electromechanical properties of ferroelectric materials. Statistically, they can be seen as random pinning centers acting on an elastic manifold, slowing domain-wall propagation and raising the energy required to switch polarization. Here we show that the "dressing" of defects can lead to unprecedented control of domain-wall dynamics. We engineer defects of two different dimensionalities in ferroelectric oxide thin films-point defects externally induced via He^{2+} bombardment, and extended quasi-one-dimensional a domains formed in response to internal strains. The a domains act as extended strong pinning sites (as expected) imposing highly localized directional constraints. Surprisingly, the induced point defects in the He^{2+} bombarded samples orient and align to impose further directional pinning, screening the effect of a domains. This defect interplay produces more uniform and predictable domain-wall dynamics. Such engineered interactions between defects are crucial for advancements in ferroelectric devices.

Power-law distributions provide a general description of diverse natural phenomena in which events with a logarithmically increasing size occur with logarithmically decreasing probability. However, experimentally derived correlated two-dimensional information is often difficult to cleanly interpret as discrete events of defined size. Moreover, physical limitation of techniques such as those based on scanning probe microscopy, which can ideally be used to observe power-law behavior, reduce event number and thus render straightforward power-law fits even more challenging. Here we develop and compare different techniques to analyze event distributions from two-dimensional images. We show that tracking interface position allows the associated scaling parameters to be accurately extracted from both experimental and synthetic image-based datasets. We also show how these techniques can differentiate between power-law and non-power-law behavior by comparison of Hill, moments, and kernel estimators of this scaling parameter. We thus present computational tools to analyze power-law fits in two-dimensional datasets and identify the scaling parameters that best describe these distributions.