Memristive synapses with high reproducibility for flexible neuromorphic networks based on biological nanocomposites

Memristive synapses from biomaterials are promising for building flexible and implantable artificial neuromorphic systems due to their remarkable mechanical and biological properties. However, these biological devices have relatively poor memristive switching characteristics, and thus fail to meet t...

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Bibliographic Details
Published in:Nanoscale 2020-01, Vol.12 (2), p.72-73
Main Authors: Ge, Jun, Li, Dongyuan, Huang, Changqiao, Zhao, Xuanbo, Qin, Jieli, Liu, Huanyu, Ye, Weiyong, Xu, Wenchao, Liu, Zhiyu, Pan, Shusheng
Format: Article
Language:English
Subjects:
Bending machines
Biological properties
Biomedical materials
Carrageenan
Endurance
Linearity
Memory devices
Nanocomposites
Reliability aspects
Reproducibility
Resistance
Self-assembly
Surgical implants
Switching theory
Synapses
Synergistic effect
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Summary:Memristive synapses from biomaterials are promising for building flexible and implantable artificial neuromorphic systems due to their remarkable mechanical and biological properties. However, these biological devices have relatively poor memristive switching characteristics, and thus fail to meet the requirement of neuromorphic networks for high learning accuracy. Here, memristive synapses based on carrageenan nanocomposites that possess desirable characteristics are demonstrated. These devices show highly reproducible analog resistive switching behaviors with 250 conductance states, low write noise, good write linearity, high retention of more than 10 4 s and endurance for at least 10 6 pulses. The enhanced switching properties are attributed to controllable and confined conductive filament growth, owing to the synergistic effect of self-assembled silver nanocluster doping and nanocone-shaped electrode contact. Moreover, the devices exhibit excellent reliability after 1000 bending cycles. Simulations including the non-ideal factors prove that the synaptic device array can operate with an online learning accuracy of 94.3%. These findings enable broader applications of biomaterials in flexible memristive devices and neuromorphic systems. A memristive synapse based on novel biomaterial nanocomposites is proposed and simulations including the non-ideal factors prove an online learning accuracy of 94.3%.
ISSN:2040-3364
2040-3372
DOI:10.1039/c9nr08001e