Collective capacitive and memristive responses in random nanowire networks: Emergence of critical connectivity pathways

Abstract

Random nanowire networks (NWNs) are promising synthetic architectures for non-volatile memory devices and hardware-based neuromorphic applications due to their history-dependent re- sponses, recurrent connectivity, and neurosynaptic-like behaviours. Such brain-like functions occur due to emergent resistive switching phenomena taking place in the interwire junctions which are viewed as memristive systems; they operate as smart analogue switches whose resistance depends on the history of the input voltage/current. We successfully demonstrated that NWNs made with a particular class of memristive junctions can exhibit a highly-selective conduction mechanism which uses the lowest-energy connectivity path in the network identified as the “winner-takes-all” state. The complex and adaptive behaviour of these junctions lead the system to channel the current through a single conductive path that spans the source-drain electrodes sandwiching the NWN. But these complex networks do not always behave in the same fashion; in the limit of sufficiently low input currents (preceding this selective conduction regime), the system behaves as a leakage capacitive network and its electrical activation is driven by cascades of breakdown-based switch- ing events involving binary capacitive transitions. Understanding these two regimes is crucial to establish the potential of these materials for neuromorphics and for this we present two computa- tional modelling schemes designed to describe the capacitive and memristive responses of NWNs interrogated adiabatically by voltage/current sources. In particular, our capacitive network model is regarded as a parallel RC circuit, with a leakage current term, to simulate their non-ideal ca- pacitive properties. Our findings reveal the fault-tolerant aspect in the slow-switching dynamics of memristive networks in contrast with the abrupt activation response obtained in the fast-switching process of binary capacitive networks. Our results are corroborated by experimental evidence that reveal the fine electrical properties of NWN materials in their respective formation (capacitive) and conducting (memristive) stages.