Spinodal Decomposition-Driven Endurable Resistive Switching in Perovskite Oxides

Universal fast mode regime in wetting kinetics

We present simulation results from a comprehensive molecular dynamics (MD) study of surface-directed spinodal decomposition (SDSD) in unstable symmetric binary mixtures at wetting surfaces. We consider long-ranged and short-ranged surface fields to investigate the early stage wetting kinetics. The attractive part of the long-ranged potential is of the form V(z)∼z^{-n}, where z is the distance from the surface and n is the power-law exponent. We find that the wetting-layer thickness R_{1}(t) at very early times exhibits a power-law growth with an exponent α=1/(n+2). It then crosses over to a universal fast-mode regime with α=3/2. In contrast, for the short-ranged surface potential, a logarithmic behavior in R_{1}(t) is observed at initial times. Remarkably, similar rapid growth is seen in this case too. We provide phenomenological arguments to understand these growth laws. Our MD results firmly establish the existence of universal fast-mode kinetics and settle the related controversy.

Nanostructured perovskites for nonvolatile memory devices

Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.