Detection and control of nanoscale ferroelectricity

Ferroelectricity refers to the property of certain materials to possess a permanent dielectric polarization that can be switched between two stable states by the application of an external electric field. Ferroelectric materials can be spontaneously organized into domains, i.e. regions of uniform polarization. Domains can also be artificially created by applying a sufficiently large electric field along which the polarization aligns. The boundary between two domains of different polarization is extremely fine: its thickness is of the order of the atomic plane. Ferroelectric materials are therefore serious candidates for information storage devices, such as non-volatile RAM memories, but they also have applications in sensors (temperature, pressure, etc.), non-linear optics and electronics.

In this context, it is crucial to be able to map ferroelectric polarization with a spatial resolution compatible with the targeted applications, i.e. the nanometer scale.

This article describes the most widely used technique for detecting and locally modifying ferroelectric polarization with nanometric resolution. Called "Piezoresponse Force Microscopy", it derives from the broader family of atomic force microscopies and is based on the measurement of the vibration due to the inverse piezoelectric effect, under the action of an electrical voltage imposed between the bottom electrode of the sample and the tip of the microscope (which represents a nanometer-sized electrode). This enables the vertical or lateral component of polarization to be mapped, as well as local hysteresis cycles to be performed by stopping the tip above a given zone, and the state of polarization to be modified locally by applying positive or negative DC voltages between tip and sample. This technique is described in detail, together with the theory behind it. The various operating modes are presented. Possible artifacts are also described and explained, such as electrostatic interaction and ionic conduction, which can generate spurious signals and distort the interpretation of data obtained by the technique. Best practice advice is given on how to make the most of the method and avoid over-interpretation of the results it provides.