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Brain like Artificial Neural Network Based on Superconducting Neurons and Synapses

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dc.contributor.author SIDORENKO, Anatolie
dc.date.accessioned 2023-11-01T12:18:48Z
dc.date.available 2023-11-01T12:18:48Z
dc.date.issued 2023
dc.identifier.citation SIDORENKO, Anatolie. Brain like Artificial Neural Network Based on Superconducting Neurons and Synapses. In: 6th International Conference on Nanotechnologies and Biomedical Engineering, ICNBME, September 20-23, 2023, Chisinau: Abstract Book, pp. 40-41. ISBN 978-9975-72-773-0. en_US
dc.identifier.isbn 978-9975-72-773-0
dc.identifier.uri http://repository.utm.md/handle/5014/24588
dc.description.abstract Energy efficiency and the radically reduction of the power consumption level becomes a crucial parameter constraining the advance of supercomputers. The most promising solution is design and development of the Brain-like systems with non-von Neumann architectures, first of all – the Artificial Neural Networks (ANN) based on superconducting elements. Superconducting ANN needs elaboration of two main elements – nonlinear switch, neuron [1] and linear connecting element, synapse [2]. We present results of our design and investigation of artificial neurons, based on superconducting spin valves – S/F/S Josephson Junctions with weak link F fabricated from magnetic material (Ni or alloy CuNi), and superconducting synapse based on layered hybrid structures superconductor-ferromagnet. We obtained and analyzed results of experimental study of the proximity effect in a stack-like superconductor/ferromagnet (S/F) superlattices Nb/Co with F = Co ferromagnetic layers of different thicknesses and coercive fields, and S= Nb superconducting layers of constant thickness equal to coherence length of niobium which can serve as an artificial synapse. The superlattices Nb/Co demonstrate change of the superconducting order parameter in thin niobium films due to switching from the parallel to the antiparallel alignment of neighboring ferromagnetic layers magnetization. We argue that such superlattices can be used as tunable kinetic inductors for ANN synapses engineering. As the result of design of the ANN using that two elaborated base elements, artificial neurons and artificial synapses, allows construction of the computer with 6-7 orders of magnitude lower energy consumption in comparison with the traditional computer designed from semiconducting base elements. The study was supported by the Grant RSF No. 22-79-10018 “Controlled kinetic inductance based on superconducting hybrid structures with magnetic materials” (theory development, samples measurements, results evaluation), and by the Moldova State Program Project «Functional nanostructures and nanomaterials for industry and agriculture» no. 20.80009.5007.11 (samples fabrication and characterization). en_US
dc.language.iso en en_US
dc.publisher Universitatea Tehnică a Moldovei en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject energy efficiency en_US
dc.subject Brain-like systems en_US
dc.subject Artificial Neural Networks (ANN) en_US
dc.title Brain like Artificial Neural Network Based on Superconducting Neurons and Synapses en_US
dc.type Article en_US


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  • 2023
    6th International Conference on Nanotechnologies and Biomedical Engineering, September 20–23, 2023, Chisinau, Moldova

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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