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 favorable 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 environmentally 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 behaviors, 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.

Department(s)

Electrical and Computer Engineering

Comments

National Science Foundation, Grant ECCS-2104976

Keywords and Phrases

biodegradable; memristor; natural organic material; neuromorphic systems; spike-rate dependent plasticity; spike-timing dependent plasticity; spiking neural network

International Standard Serial Number (ISSN)

1361-6463; 0022-3727

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 IOP Publishing, All rights reserved.

Publication Date

02 Jun 2022

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