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

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Abstract

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 104 s and endurance for at least 106 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.

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

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Publication details

The article was received on 17 Sep 2019, accepted on 03 Dec 2019 and first published on 03 Dec 2019


Article type: Paper
DOI: 10.1039/C9NR08001E
Nanoscale, 2020, Advance Article

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

    J. Ge, D. Li, C. Huang, X. Zhao, J. Qin, H. Liu, W. Ye, W. Xu, Z. Liu and S. Pan, Nanoscale, 2020, Advance Article , DOI: 10.1039/C9NR08001E

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