An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse

By Fabien Alibart, S. Pleutin, D. Guérin, Christophe Novembre, S. Lenfant, K. Lmimouni, Christian Gamrat, D. Vuillaume. 2009

351 citations recorded by the source corpus.

Molecule‐based devices are envisioned to complement silicon devices by providing new functions or by implementing existing functions at a simpler process level and lower cost, by virtue of their self‐organization capabilities. Moreover, they are not bound to von Neuman architecture and this feature may open the way to other architectural paradigms. Neuromorphic electronics is one of them. Here, a device made of molecules and nanoparticles—a nanoparticle organic memory field‐effect transistor (NOMFET)—that exhibits the main behavior of a biological spiking synapse is demonstrated. Facilitating and depressing synaptic behaviors can be reproduced by the NOMFET and can be programmed. The synaptic plasticity for real‐time computing is evidenced and described by a simple model. These results open the way to rate‐coding utilization of the NOMFET in dynamical neuromorphic computing circuits.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/31840b28400373be2887d309ae56780534079a7d

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In Artificial Intelligence

1. Shimeng Yu
2. H. Philip Wong
3. Roberto Osellame
4. Andrea Crespi
5. Philip Walther

In Computational Theory and Mathematics

1. Philip Walther

In Electrical and Electronic Engineering

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8. Alberto Salleo

Citations in the corpus

• Synaptic electronics: materials, devices and applications (2013, background): “Alibart et al demonstrated potentiation and depression type synaptic behaviors in nanoparticle–organic field effect transistors (NOMFETs) [82].”
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, neutral): “[2326]–[2331] and organic nanoparticle transistors [2332]–”
• Experimental photonic quantum memristor (2021, background): “updated via a discrete-time equivalent of equation (9).”
• Artificial neural networks in hardware: A survey of two decades of progress (2010, neutral): “[278]”