Honey-CNT memristive artificial synaptic device for sustainable neuromorphic computing system

Abstract Brain-inspired neuromorphic computing systems require hardware components analogous to biological neurons and synapses. Honey based natural organic memristor has demonstrated promising nonvolatile memristive behaviors, with the advantages of sustainability, environmentally friendliness, and low-cost manufacturing. In this study, carbon nanotubes (CNTs) are added in honey to fabricate honey-CNT memristive artificial synaptic devices. Honey-CNT film is characterized by micro-Raman spectroscopy and the distribution of CNT bundles embedded in the honey-CNT composite layer by cross-sectional scanning electron microscopy for the first time. Critical synaptic functions of the honey-CNT memristor, including spike-rate-dependent plasticity, spike voltage dependent plasticity, learn-forget-relearn, and supralinear spatial summation are revealed, which have not been reported by honey based memristive devices before. Paired pulse facilitation with a PPF index as large as 4.5 is observed, indicating the enhancement of synaptic weight by CNTs. Furthermore, honey-CNT memristor based neuromorphic system is evaluated in terms of linearity, accuracy, read/write energy, and overall performance by the image recognition using Stochastic Gradient Descent and Adaptive Moment Estimation learning algorithms and the Modified National Institute of Standards and Technology database.