DocumentCode :
165836
Title :
Electro-grafted organic memristors: Properties and prospects for artificial neural networks based on STDP
Author :
Cabaret, T. ; Fillaud, L. ; Jousselme, B. ; Klein, Jacques-Olivier ; Derycke, V.
Author_Institution :
CEA Saclay, IRAMIS, Gif-sur-Yvette, France
fYear :
2014
fDate :
18-21 Aug. 2014
Firstpage :
499
Lastpage :
504
Abstract :
The capabilities of memristors to serve as artificial synapses in neural network type of circuits have been recently recognized. These two-terminal analog memory devices offer valuable advantages in terms of circuit architectures. In particular, with their room temperature processes and large diversity coming from chemistry, organic memristors represent a chance to develop devices that can be densely integrated above-IC. In this article, we present a new class of organic resistive memory based on a robust electrografted redox thin film as active material integrated in a planar metal/organic/metal topology. The combination of a specific redox polymer and of the electro-grafting technique leading to fully covalent films makes such organic memristors particularly robust. The devices display high RMAX/RMIN ratio, long retention time and multi-level conductivity. The potential of these devices to store analog synaptic weights in neural network circuit strategies is shown by demonstrating their compatibility with the Spike Timing Dependence Plasticity (STDP) learning rule and by implementing the associative memory function.
Keywords :
content-addressable storage; electronic engineering computing; learning (artificial intelligence); memristors; neural nets; oxidation; polymer films; reduction (chemical); thin film circuits; STDP learning rule; analog synaptic weight storage; artificial neural network circuit; associative memory function; covalent film; electrografted organic memristor; multilevel conductivity; organic resistive memory; planar metal-organic-metal topology; redox polymer; robust electrografted redox thin film; spike timing dependence plasticity learning rule; temperature 293 K to 298 K; two-terminal analog memory device; Conductivity; Delays; Electrodes; Evolution (biology); Memristors; Metals; Neurons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nanotechnology (IEEE-NANO), 2014 IEEE 14th International Conference on
Conference_Location :
Toronto, ON
Type :
conf
DOI :
10.1109/NANO.2014.6968169
Filename :
6968169
Link To Document :
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