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Cellulose Nanocrystal Based Bio‐Memristor as a Green Artificial Synaptic Device for Neuromorphic Computing Applications.

Authors :
Hussain, Tassawar
Abbas, Haider
Youn, Chulmin
Lee, Hojin
Boynazarov, Turgun
Ku, Boncheol
Jeon, Yu‐Rim
Han, Hoonhee
Lee, Jong Hyeon
Choi, Changhwan
Choi, Taekjib
Source :
Advanced Materials Technologies. Feb2022, Vol. 7 Issue 2, p1-13. 13p.
Publication Year :
2022

Abstract

Nanocomposites based on biomaterials are promising candidates for emerging green‐ electronics benefiting from environment‐friendly, renewable, biocompatible, and biodegradable resources for sustainable research and development. Especially, the application of biocomposites‐based memristor for simulating artificial synapses called bio‐memristor has further facilitated the progress of ecologically benign bioelectronics. In this study, the authors present that the environment‐friendly nanocomposites films, consisting of Ag nanoparticles and cellulose nanocrystal (CNC)‐based bio‐memristor with excellent bipolar resistive switching behavior can perform the artificial bio‐synaptic emulation with continuous resistance modulation for memory storage and neuromorphic computing applications. The bio‐memristor exhibits a large resistive switching (ION/OFF as high as ≈104 and ultralow SET/RESET voltage of ≈0.2 V) and reliable switching characteristics through the electrochemical formation/rupture of Ag metallic filaments within the nanocomposite layer. The device presents coexistence of digital and analog switching properties favorable for both nonvolatile digital memory and neuromorphic computing applications. By applying appropriate pulse stimulations to the device, the authors demonstrate biological synaptic functions, including long‐term potentiation/depression, spike‐rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and paired‐pulse depression. Thus, this CNC‐based bio‐memristor as an effective artificial synaptic device is beneficial towards the realization of green‐electronics and bio‐inspired neuromorphic systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2365709X
Volume :
7
Issue :
2
Database :
Academic Search Index
Journal :
Advanced Materials Technologies
Publication Type :
Academic Journal
Accession number :
155130900
Full Text :
https://doi.org/10.1002/admt.202100744