A Natural Organic Artificial Synaptic Device Made from a Honey and Carbon Nanotube Admixture for Neuromorphic Computing

被引:12
|
作者
Tanim, Md Mehedi Hasan [1 ]
Templin, Zoe [1 ]
Hood, Kaleb [2 ]
Jiao, Jun [2 ]
Zhao, Feng [1 ]
机构
[1] Washington State Univ, Sch Engn & Comp Sci, Micro Nanoelect & Energy Lab, Vancouver, WA 98686 USA
[2] Portland State Univ, Dept Mech & Mat Engn, Mat Sci & Nanodevice Lab, Portland, OR 97201 USA
来源
ADVANCED MATERIALS TECHNOLOGIES | 2023年 / 8卷 / 14期
基金
美国国家科学基金会;
关键词
artificial synapse; carbon nanotube; honey; memristor; neuromorphic computing; MEMORY; RAMAN; SPECTROSCOPY; MECHANISMS; CONDUCTION; FILM;
D O I
10.1002/admt.202202194
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Artificial synaptic devices are the essential hardware component in emerging neuromorphic computing systems by mimicking biological synapse and brain functions. When made from natural organic materials such as protein and carbohydrate, they have potential to improve sustainability and reduce electronic waste by enabling environmentally-friendly disposal. In this paper, a new natural organic memristor based artificial synaptic device is reported with the memristive film processed by a honey and carbon nanotube (CNT) admixture, that is, honey-CNT memristor. Optical microscopy, scanning electron microscopy, and micro-Raman spectroscopy are employed to analyze the morphology and chemical structure of the honey-CNT film. The device demonstrates analog memristive potentiation and depression, with the mechanism governing these functions explained by the formation and dissolution of conductive paths due to the electrochemical metal filaments which are assisted by CNT clusters and bundles in the honey-CNT film. The honey-CNT memristor successfully emulates synaptic functionalities such as short-term plasticity and its transition to long-term plasticity for memory rehearsal, spatial summation, and shunting inhibition, and for the first time, the classical conditioning behavior for associative learning by mimicking the Pavlov's dog experiment. All these results testify that honey-CNT memristor based artificial synaptic device is promising for energy-efficient and eco-friendly neuromorphic systems.
引用
收藏
页数:10
相关论文
共 27 条
  • [1] An organic artificial synaptic memristor for neuromorphic computing
    Gao, Kaikai
    Sun, Bai
    Yang, Bo
    Cao, Zelin
    Cui, Yu
    Wang, Mengna
    Kong, Chuncai
    Zhou, Guangdong
    Luo, Sihai
    Chen, Xiaoliang
    Shao, Jinyou
    APPLIED MATERIALS TODAY, 2025, 43
  • [2] Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems
    Tanim, Md Mehedi Hasan
    Templin, Zoe
    Zhao, Feng
    MICROMACHINES, 2023, 14 (02)
  • [3] Carbon Nanotube-Based Flexible Ferroelectric Synaptic Transistors for Neuromorphic Computing
    Xia, Fan
    Xia, Tian
    Xiang, Li
    Ding, Sujuan
    Li, Shuo
    Yin, Yucheng
    Xi, Meiqi
    Jin, Chuanhong
    Liang, Xuelei
    Hu, Youfan
    ACS APPLIED MATERIALS & INTERFACES, 2022, : 30124 - 30132
  • [4] Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing
    Esqueda, Ivan Sanchez
    Yan, Xiaodong
    Rutherglen, Chris
    Kane, Alex
    Cain, Tyler
    Marsh, Phil
    Liu, Qingzhou
    Galatsis, Kosmas
    Wang, Han
    Zhou, Chongwu
    ACS NANO, 2018, 12 (07) : 7352 - 7361
  • [5] Memristive synaptic device based on a natural organic material-honey for spiking neural network in biodegradable neuromorphic systems
    Sueoka, Brandon
    Zhao, Feng
    JOURNAL OF PHYSICS D-APPLIED PHYSICS, 2022, 55 (22)
  • [6] Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing
    Hu, Xuemeng
    Meng, Jialin
    Feng, Tianyang
    Wang, Tianyu
    Zhu, Hao
    Sun, Qingqing
    Zhang, David Wei
    Chen, Lin
    NANO RESEARCH, 2024, 17 (06) : 5614 - 5620
  • [7] Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing
    Xuemeng Hu
    Jialin Meng
    Tianyang Feng
    Tianyu Wang
    Hao Zhu
    Qingqing Sun
    David Wei Zhang
    Lin Chen
    Nano Research, 2024, 17 : 5614 - 5620
  • [8] Spin-Charge Conversion-Based Artificial Synaptic Device for Neuromorphic Computing
    Kim, Seong Been
    Lee, Je-Jun
    Choi, Dongwon
    Kim, Seung-Hwan
    Ahn, Jeong Ung
    Han, Ki Hyuk
    Park, Tae-Eon
    Lee, OukJae
    Lee, Ki-Young
    Hong, Seokmin
    Min, Byoung-Chul
    Kim, Hyung-Jun
    Hwang, Do Kyung
    Koo, Hyun Cheol
    ACS Applied Electronic Materials, 2025, 7 (01) : 571 - 581
  • [9] CMOS-Compatible Embedded Artificial Synaptic Device (eASD) for Neuromorphic Computing and AI Applications
    Huang, Yao-Hung
    Yu, Hsin-Yuan
    Chih, Yue-Der
    Wang, Yih
    King, Ya-Chin
    Lin, Chrong Jung
    IEEE TRANSACTIONS ON ELECTRON DEVICES, 2024, 71 (02) : 1313 - 1319
  • [10] Parallel weight update protocol for a carbon nanotube synaptic transistor array for accelerating neuromorphic computing
    Kim, Sungho
    Lee, Yongwoo
    Kim, Hee-Dong
    Choi, Sung-Jin
    NANOSCALE, 2020, 12 (03) : 2040 - 2046