Electro-grafted Organic Memristors as Synapses: Spike Timing-Dependent Plasticity and Supervised Function Learning
Abstract
Neuromorphic computing is an efficient way to handle complex tasks such as image recognition and
classification. Hardware implementation of an artificial neural network (ANN) requires arrays of scalable
memory elements to act as artificial synapses. Memristors, which are two-terminal analog memory devices,
are excellent candidates for this application as their tunable resistance could be used to code and store
synaptic weights. We studied metal-organic-metal memristors in which the organic layer is a dense and
robust electro-grafted thin film of redox complexes. The process allows fabricating planar and vertical
junctions, as well as small crossbar arrays. The unipolar devices display non-volatile multi-level
conductivity states with high Rmax/Rmin ratio and two thresholds. We characterized in depth the
characteristics of individual memristors with respect to the targeted synaptic function. We notably showed
that they possess the Spike Timing-Dependent Plasticity (STDP) property (their conductivity evolves as a
function of the time-delay between incoming pulses at both terminals), which is critical for future
applications in neuromorphic circuits based on unsupervised learning. In parallel, we implemented
memristors as synapses in a simple prototype: a mixed circuit with the neuron implemented with
conventional electronics. This ANN is able to learn linearly separable 3-input logic functions through an
iterative supervised learning algorithm inspired by the Widrow-Hoff rule.
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