Journal
NANO LETTERS
Volume 19, Issue 2, Pages 839-849Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.8b04023
Keywords
Flexible memristor; artificial neural network (ANN); neuromorphic system; quantized conductance; electrochemical metallization (ECM)
Categories
Funding
- Global Frontier Center for Advanced Soft Electronics [CASE-2011-0031640, CASE-2017M3A6A5052509]
- Brain Korea 21 Plus Project of School of Electrical Engineering of KAIST in 2018
- Creative Research Program of the ETRI [18ZB11400]
- Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource [NSF ECCS-1542205]
- MRSEC program at the Materials Research Center [NSF DMR-1121262]
- International Institute for Nanotechnology (IIN)
- Keck Foundation
- state of Illinois through the TIN
Ask authors/readers for more resources
With the advent of artificial intelligence (AI), memristors have received significant interest as a synaptic building block for neuromorphic systems, where each synaptic memristor should operate in an analog fashion, exhibiting multilevel accessible conductance states. Here, we demonstrate that the transition of the operation mode in poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament. With the quantized conductance states observed in the flexible pV3D3 memristor, analog potentiation and depression characteristics of the memristive synapse are obtained through the growth of atomically thin Cu filament and lateral dissolution of the filament via dominant electric field effect, respectively. The face classification capability of our memristor is evaluated via simulation using an artificial neural network consisting of pV3D3 memristor synapses. These results will encourage the development of soft neuromorphic intelligent systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available