A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing

TL;DR

This paper introduces a novel memristive hybrid synapstor device based on nanoparticle/organic materials that emulates biological synapses and demonstrates spike-timing-dependent plasticity for neuro-inspired computing.

Contribution

It presents a new nanoparticle/organic hybrid synapstor device capable of STDP learning, integrating memristive properties with compatibility to CMOS platforms.

Findings

Demonstrated STDP in a nanoparticle/organic hybrid device

Showed shape-dependent tuning of the learning function

Coupled the synapstor with CMOS neuron circuits

Abstract

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 (synapse-transistor), called Nanoparticle Organic Memory Field Effect Transistor (NOMFET). We show that this learning function is obtained with a simple hybrid material made of the self-assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, we also demonstrate how the shape of the applied spikes can tailor the STDP 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.