Electro-grafted Organic Memristors as Synapses: Spike Timing-Dependent Plasticity and Supervised Function Learning - Archive ouverte HAL Access content directly
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Electro-grafted Organic Memristors as Synapses: Spike Timing-Dependent Plasticity and Supervised Function Learning

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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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Dates and versions

cea-02346368 , version 1 (05-11-2019)

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  • HAL Id : cea-02346368 , version 1

Cite

Yu-Pu Lin, Christopher H. Bennett, Damir Vodenicarevic, Djaafar Chabi, Damian Querlioz, et al.. Electro-grafted Organic Memristors as Synapses: Spike Timing-Dependent Plasticity and Supervised Function Learning. E-MRS 2016 Spring Meeting, May 2016, Lille, France. ⟨cea-02346368⟩
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