Abstract
Networks of nanoscale objects are the subject of increasing interest as resistive switching systems for the fabrication of neuromorphic computing architectures. Nanostructured films of bare gold clusters produced in gas phase with thickness well beyond the electrical percolation threshold, show a non-ohmic electrical behavior and resistive switching, resulting in groups of current spikes with irregular temporal organization. Here we report the systematic characterization of the temporal correlations between single spikes and spiking rate power spectrum of nanostructured Au two-terminal devices consisting of a cluster-assembled film deposited between two planar electrodes. By varying the nanostructured film thickness we fabricated two different classes of devices with high and low initial resistance respectively. We show that the switching dynamics can be described by a power law distribution in low resistance devices whereas a bi-exponential behavior is observed in the high resistance ones. The measured resistance of cluster-assembled films shows a scaling behavior in the range of analyzed frequencies. Our results suggest the possibility of using cluster-assembled Au films as components for neuromorphic systems where a certain degree of stochasticity is required.
Highlights
Significant performance improvements of computational systems based on von Neumann architecture, in terms of data handling and processing, are hardly sustainable at extreme miniaturization due to the required energy dissipation capabilities [1,2,3,4]
We show that the switching dynamics can be described by a power law distribution in low resistance devices whereas a bi-exponential behavior is observed in the high resistance ones
Two-terminal devices based on nanostructured Au films were fabricated by Supersonic Cluster Beam Deposition [32]: the deposition apparatus is equipped with a Pulsed Microplasma Cluster Source (PMCS) that allows the production of neutral clusters in gas phase as described in details in [33]
Summary
Significant performance improvements of computational systems based on von Neumann architecture, in terms of data handling and processing, are hardly sustainable at extreme miniaturization due to the required energy dissipation capabilities [1,2,3,4]. An approach aiming at the reproduction of the human brain architectural and dynamical properties has been proposed to overcome these limitations [3, 5]. Neurons and synapses are the two basic computational units in the brain: a neuron receives the inputs coming from other neurons and, on the basis of its own kind of activation function, can in turn generate electrical spikes as an output [6]. Synapses are the further key factor for the overall computation capability of a neuronal system [6, 7]. The synaptic strength between neurons, i.e. of their connections, is modified by both repeated and/or modified neural activity both in pre-synaptic and post-synaptic cells (Hebb’s postulate) [6].
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