Synaptic plasticity emulation by natural biomaterial honey-CNT-based memristors

Author:

Templin Zoe1ORCID,Tanim Md Mehedi Hasan1ORCID,Zhao Feng1ORCID

Affiliation:

1. Micro/Nanoelectronic and Energy Laboratory, School of Engineering and Computer Science, Washington State University , Vancouver, Washington 98686, USA

Abstract

Artificial synaptic devices made from natural biomaterials capable of emulating functions of biological synapses, such as synaptic plasticity and memory functions, are desirable for the construction of brain-inspired neuromorphic computing systems. The metal/dielectric/metal device structure is analogous to the pre-synapse/synaptic cleft/post-synapse structure of the biological neuron, while using natural biomaterials promotes ecologically friendly, sustainable, renewable, and low-cost electronic devices. In this work, artificial synaptic devices made from honey mixed with carbon nanotubes, honey-carbon nanotube (CNT) memristors, were investigated. The devices emulated spike-timing-dependent plasticity, with synaptic weight as high as 500%, and demonstrated a paired-pulse facilitation gain of 800%, which is the largest value ever reported. 206-level long-term potentiation (LTP) and long-term depression (LTD) were demonstrated. A conduction model was applied to explain the filament formation and dissolution in the honey-CNT film, and compared to the LTP/LTD mechanism in biological synapses. In addition, the short-term and long-term memory behaviors were clearly demonstrated by an array of 5 × 5 devices. This study shows that the honey-CNT memristor is a promising artificial synaptic device technology for applications in sustainable neuromorphic computing.

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

Physics and Astronomy (miscellaneous)

Reference38 articles.

1. Towards reliable in-memory computing: From emerging devices to post-von-Neumann architectures,2021

2. Electronic waste management approaches: An overview;Waste Manage.,2013

3. Status of electronic waste recycling techniques: A review;Environ. Sci. Pollut. Res.,2018

4. Neuromorphic computing using non-volatile memory;Adv. Phys. X,2017

5. Opportunities for neuromorphic computing algorithms and applications;Nat. Comput. Sci.,2022

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3