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  • Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems
Title: Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems
Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biological synapse with low power consumption, fast switching speed and simulation of synaptic plasticity, while natural organic materials are water soluble, renewable and environmental friendly. In this study, the potential of a natural organic material—honey-based memristor for SNNs was demonstrated. The device exhibited forming-free bipolar resistive switching, a high switching speed of 100 ns set time and 500 ns reset time, STDP and SRDP learning behaviours, and dissolving in water. The intuitive conduction models for STDP and SRDP were proposed. These results testified that honey-based memristive synaptic devices are promising for SNN implementation in green electronics and biodegradable neuromorphic systems.  more » « less
Award ID(s):
2104976
NSF-PAR ID:
10323895
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Physics D: Applied Physics
Volume:
55
Issue:
22
ISSN:
0022-3727
Page Range / eLocation ID:
225105
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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