Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

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 selftune to balanced states consistent with self-organised criticality. Our simulations allow visualisation of the network states and detailed mechanisms of signal propagation.

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

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.

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An oxide-based resistance memory is a leading candidate to replace Si-based flash memory as it meets the emerging specifications for future memory devices. The non-uniformity in the key switching parameters and low endurance in conventional resistance memory devices are preventing its practical application. Here, a novel strategy to overcome the aforementioned challenges has been unveiled by tuning the growth direction of epitaxial brownmillerite SrFeO thin films along the SrTiO [111] direction so that the oxygen vacancy channels can connect both the top and bottom electrodes rather directly. The controlled oxygen vacancy channels help reduce the randomness of the conducting filament (CF). The resulting device displayed high endurance over 10 cycles, and a short switching time of ∼10 ns. In addition, the device showed very high uniformity in the key switching parameters for device-to-device and within a device. This work demonstrates a feasible example for improving the nanoscale device performance by controlling the atomic structure of a functional oxide layer.

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We had discovered novel resistance switching phenomena in SrCoOx epitaxial thin films. We have interpreted the results in terms of the topotactic phase transformation between their insulating brownmillerite phase and the conducting perovskite phase and the existence of a rather vertical conducting filament due to its inherent layered structure. However, the rough interface observed between the SrCoOx and the Au top electrode (area ~10000 μm2) was assumed to result in the observed fluctuation in key switching parameters. In order to verify the effect of rough interface on the switching performance in the SrCoOx device, in this work, we studied the resistive switching properties of a SrCoOx device by placing a Au-coated tip (end area ~0.5 μm2) directly on the film surface as the top electrode. The resulting device displayed much improved endurance and showed high uniformity in key switching parameters as compared to the device having a large top electrode area. A simulation result confirmed that the Au-coated tip provides a local confinement of the electrical field, resulting in confinement of oxygen ion distribution and therefore localization of the conducting filament. By minimizing other free and uncontrollable parameters, the designed experiment here provides the most direct and isolated evidence that the rough interface between electrode and ReRAM matrix is detrimental for the reproducibility of resistivity switching phenomena.