Networks of silver nanowires appear set to replace expensive indium tin oxide as the transparent conducting electrode material in next generation devices. The success of this approach depends on optimising the material conductivity, which up to now has largely focused on minimising the junction resistance between wires. However, there have been no detailed reports on what the junction resistance is, nor is there a known benchmark for the minimum attainable sheet resistance of an optimised network. In this paper we present junction resistance measurements of individual silver nanowire junctions, producing for the first time a distribution of junction resistance values, and conclusively demonstrating that the junction contribution to the overall resistance can be reduced beyond that of the wires themselves through standard processing techniques. We

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a “winner-takes-all” conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing.

Connectivity in metallic nanowire networks with resistive junctions is manipulated by applying an electric field to create materials with tunable electrical conductivity. In situ electron microscope and electrical measurements visualize the activation and evolution of connectivity within these networks. Modeling nanowire networks, having a distribution of junction breakdown voltages, reveals universal scaling behavior applicable to all network materials. We demonstrate how local connectivity within these networks can be programmed and discuss material and device applications.

We report the deposition and characterization of thin networks of gold nanowires on plastic substrates. The average nanowire diameter was 47 nm, while the networks had mean thicknesses in the range of 35–750 nm. The conductivity of networks with mean thicknesses below 100 nm was controlled by percolation, as characterized by the percolation exponent, n = 0.8, and the percolative figure of merit, Π = 28. However, networks with thicknesses above 100 nm had thickness-independent direct current conductivity of σDC,B = 5.4 × 105 S/m. The conductivity was limited by junction resistances, which we estimate at ∼3 kΩ. Networks of all thicknesses were described by an optical conductivity of σOp = 1.0 × 104 S/m. These electrical and optical properties result in networks with sheet resistance and transmittance very close to industry requirements, that is, 49 Ω/□ @ 83%. These values are superior to almost all reported carbon nanotube networks and are competitive with silver nanowire networks. We measured the work function of these networks to be 4.6 eV, suggesting them to be suitable for hole injection or collection in electronic devices.

In this work, we introduce a combined experimental and computational approach to describe the conductivity of metallic nanowire networks. Due to their highly disordered nature, these materials are typically described by simplified models in which network junctions control the overall conductivity. Here, we introduce a combined experimental and simulation approach that involves a wire-by-wire junction-byjunction simulation of an actual network. Rather than dealing with computer-generated networks, we use a computational approach that captures the precise spatial distribution of wires from an SEM analysis of a real network. In this way, we fully account for all geometric aspects of the network, i.e. for the properties of the junctions and wire segments. Our model predicts characteristic junction resistances that are smaller than those found by earlier simplified models. The model outputs characteristic values that depend on the detailed connectivity of the network, which can be used to compare the performance of different networks and to predict the optimum performance of any network and its scope for improvement.