Neuromorphic behaviour in discontinuous metal films.

Physical systems that exhibit brain-like behaviour are currently under intense investigation as platforms for neuromorphic computing. We show that discontinuous metal films, comprising irregular flat islands on a substrate and formed using simple evaporation processes, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex. We further demonstrate that these signals meet established criteria for criticality. We perform a detailed experimental investigation of the atomic-scale switching processes that are responsible for these signals, and show that they mimic the integrate-and-fire mechanism of biological neurons. Using numerical simulations and a simple circuit model, we show that the characteristic features of the switching events are dependent on the network state and the local position of the switch within the complex network. We conclude that discontinuous films provide an interesting potential platform for brain-inspired computing.

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic spiking behavior that is strikingly similar to that observed in biological neurons. The spiking rate can be controlled by the input stimulus, similar to "rate coding" in biology, and the distributions of times between events are log-normal, providing insights into the atomic-scale spiking mechanism. The stochasticity of the spiking behavior is then used for true random number generation, and the high quality of the generated random bit-streams is demonstrated, opening up promising routes toward integration of neuromorphic computing with secure information processing.

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation.

Epitaxial Brownmillerite Oxide Thin Films for Reliable Switching Memory.

Resistive switching memory, which is mostly based on polycrystalline thin films, suffers from wide distributions in switching parameters-including set voltage, reset voltage, and resistance-in their low- and high-resistance states. One of the most commonly used methods to overcome this limitation is to introduce inhomogeneity. By contrast, in this paper, we obtained uniform resistive switching parameters and sufficiently low forming voltage by maximizing the uniformity of an epitaxial thin film. To achieve this result, we deposited an SrFeOx/SrRuO3 heteroepitaxial structure onto an SrTiO3 (001) substrate by pulsed laser deposition, and then we deposited an Au top electrode by electron-beam evaporation. This device exhibited excellent bipolar resistance switching characteristics, including a high on/off ratio, narrow distribution of key switching parameters, and long data retention time. We interpret these phenomena in terms of a local, reversible phase transformation in the SrFeOx film between brownmillerite and perovskite structures. Using the brownmillerite structure and atomically uniform thickness of the heteroepitaxial SrFeOx thin film, we overcame two major hurdles in the development of resistive random-access memory devices: high forming voltage and broad distributions of switching parameters.