A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing

Alibart, F.; Pleutin, S.; Bichler, O.; Gamrat, C.; Serrano-Gotarredona, T.; Linares-Barranco, B.; Vuillaume, D.

Document type :

Article dans une revue scientifique

DOI :

10.1002/adfm.201101935

Title :

A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing

Author(s) :

Alibart, F. [Auteur]
Pleutin, S. [Auteur]
Bichler, O. [Auteur]
Gamrat, C. [Auteur]
Serrano-Gotarredona, T. [Auteur]
Linares-Barranco, B. [Auteur]
Vuillaume, D. [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Journal title :

Advanced Functional Materials

Pages :

609-616

Publisher :

Wiley

Publication date :

2012

ISSN :

1616-301X

English keyword(s) :

Organic electronics
hybrid materials
memristor
neuromorphic device
synaptic plasticity

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among ...
Show more >A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among various propositions, Spiking Neural Network (SNN) seems a valid candidate. (i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. (ii) In terms of technology, SNN may be able to benefit the most from nanodevices, because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here we demonstrate Spike-Timing-Dependent Plasticity (STDP), a basic and primordial learning function in the brain, with a new class of synapstor (synapsetransistor), called Nanoparticle Organic Memory Field Effect Transistor (NOMFET). We show that this learning function is obtained with a simple hybrid material made of the selfassembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, we also demonstrate how the shape of the applied spikes can tailor theSTDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre- and post-synaptic neurons, and a behavioral macro-model is developed on usual device simulator.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Non spécifiée

Popular science :

Non

Collections :