Efficient implementation of synaptic learning rules for neuromorphic computing based on plasma-treated ZnO nanowire memristors

Abstract

Abstract Nanomaterial-based memristors with analog resistive switching properties are used in the study of electronic synapses, providing information on both nanoscale device physics and low-power neuromorphic computing applications. Here, a memristor based on individual ZnO nanowires is prepared to study synaptic learning rules. Hebbian plasticity modulation is achieved with the co-application of pre- and post-synaptic spikes by tuning the temporal difference, spike frequency and voltage amplitude. Additionally, synaptic saturation is observed to stabilize the growth of synaptic weights. Plasma treatment of the memristors was performed to investigate its effects on synaptic plasticity and conductance modulation linearity during resistive switching. Plasma treatment allowed gradual conductance modulation of the memristor to be obtained, with improved conductance modulation linearity, suggesting that the memristor is capable of implementing synaptic plasticity to serve learning and memory. It was observed that the plasma treatment could also extend synaptic weight changes (Δ w ) to enhance learning capability and accelerate the learning speed of the electronic synapse, which might open up a route for modifying the characteristics of an electronic synapse. Synaptic learning and forgetting behavior are effectively simulated with re-learning of forgotten information at a much faster rate.