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Nano Research 2023, 16(7): 10500-10506 https://doi.org/10.1007/s12274-023-5744-5
Research Article | Open Access | Issue | Published: 31 May 2023

Neuromorphic nanocluster networks: Critical role of the substrate in nano-link formation

Show Author's Information Wenkai Wu1,  Alexey V. Verkhovtsev2,  Theodoros Pavloudis1,3,  Andrey V. Solov’yov2,  Richard E. Palmer1( )
Nanomaterials Lab, Faculty of Science and Engineering, Swansea University, Bay Campus, Fabian Way, SA1 8EN, Swansea, UK
MBN Research Center, Altenhöferallee 3, 60438 Frankfurt am Main, Germany
School of Physics, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Keywords:
nanoclusters, nanoparticles (NPs), synapse, molecular dynamics (MD), atomic-switch network, neuromorphics, clusters on surfaces
Topics:
Gold nanoclusters MemristorMolecular dynamicsNanoparticlesNanostructured materialsNanowiresNeuromorphic Computing
Cite this article:
Wu W, Verkhovtsev AV, Pavloudis T, et al. Neuromorphic nanocluster networks: Critical role of the substrate in nano-link formation. Nano Research, 2023, 16(7): 10500-10506. https://doi.org/10.1007/s12274-023-5744-5
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Abstract Full text About this article

Atomic cluster-based networks represent a promising architecture for the realization of neuromorphic computing systems, which may overcome some of the limitations of the current computing paradigm. The formation and breakage of links between the clusters are of utmost importance for the functioning of these computing systems. This paper reports the results of molecular dynamics simulations of synapse (bridge) formation at elevated temperature and thermal breaking processes between 2.8 nm-sized Au1415 clusters deposited on a carbon substrate, a model system. Crucially, we find that the bridge formation process is driven by the diffusion of gold atoms along the substrate, no matter how small the gap between the clusters themselves. The complementary simulations of the bridge breaking process reveal the existence of a threshold bias voltage to activate bridge rupture via Joule heating. These results provide an atomistic-level understanding of the fundamental dynamical processes occurring in neuromorphic cluster arrays.


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About this article

Neuromorphic nanocluster networks: Critical role of the substrate in nano-link formation

Show Author's information Wenkai Wu1, Alexey V. Verkhovtsev2, Theodoros Pavloudis1,3, Andrey V. Solov’yov2, Richard E. Palmer1( )
Nanomaterials Lab, Faculty of Science and Engineering, Swansea University, Bay Campus, Fabian Way, SA1 8EN, Swansea, UK
MBN Research Center, Altenhöferallee 3, 60438 Frankfurt am Main, Germany
School of Physics, Faculty of Sciences, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece

Abstract

Atomic cluster-based networks represent a promising architecture for the realization of neuromorphic computing systems, which may overcome some of the limitations of the current computing paradigm. The formation and breakage of links between the clusters are of utmost importance for the functioning of these computing systems. This paper reports the results of molecular dynamics simulations of synapse (bridge) formation at elevated temperature and thermal breaking processes between 2.8 nm-sized Au1415 clusters deposited on a carbon substrate, a model system. Crucially, we find that the bridge formation process is driven by the diffusion of gold atoms along the substrate, no matter how small the gap between the clusters themselves. The complementary simulations of the bridge breaking process reveal the existence of a threshold bias voltage to activate bridge rupture via Joule heating. These results provide an atomistic-level understanding of the fundamental dynamical processes occurring in neuromorphic cluster arrays.

Keywords: nanoclusters, nanoparticles (NPs), synapse, molecular dynamics (MD), atomic-switch network, neuromorphics, clusters on surfaces

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Publication history

Received: 16 February 2023
Revised: 13 April 2023
Accepted: 16 April 2023
Published: 31 May 2023
Issue date: July 2023

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© The author(s) 2023

Acknowledgements

Acknowledgements

The authors are grateful for partial financial support by the European Commission through the RADON project (GA 872494) within the H2020-MSCA-RISE-2019 call. This article is also based upon work from the COST Action CA20129 MultIChem, supported by COST (European Cooperation in Science and Technology). TP acknowledges Swansea University support via project 74143 of the Research Committee of Aristotle University of Thessaloniki. This work was also supported in part by Deutsche Forschungsgemeinschaft (Project no. 415716638). The authors acknowledge the computational support by the Goethe-HLR cluster of the Frankfurt Center for Scientific Computing and by the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via the Welsh Government.

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