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Ultima descărcare din IBN: 2024-05-16 17:44 |
![]() LUPU, Maria, KLENOV, Nikolai V., SOLOVIEV, Igor I., BAKURSKIY, Sergey V., BOIAN, Vladimir, MALCOCI, Cezar Casian, PREPELITSA, Andrei, ANTROPOV, Evgheni, MORARI, Roman, SIDORENKO, Anatolie. Spintronic Functional Nanostructures for Artificial Neural Network. In: Electronics, Communications and Computing, Ed. 12, 20-21 octombrie 2022, Chişinău. Chișinău: Tehnica-UTM, 2023, Editia 12, p. 24. |
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Electronics, Communications and Computing Editia 12, 2023 |
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Conferința "Electronics, Communications and Computing" 12, Chişinău, Moldova, 20-21 octombrie 2022 | |||||||
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Pag. 24-24 | |||||||
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Rezumat | |||||||
Energy consumption reduction becomes a crucial parameter constraining the advance of supercomputers. The non-von Neumann architectures, first of all – the Artificial Neural Networks (ANN) based on superconducting spintronic elements, seems to be the most promising solution. Superconducting ANN needs elaboration of two main elements – nonlinear one (neuron) [1] and linear connecting element (synapse) [2]. Results of our theoretical and experimental study of the proximity effect in a stack-like superconductor/ferromagnet (S/F) superlattice with Coferromagnetic layers of different thicknesses and coercive fields, and Nb-superconducting layers of constant thickness equal to coherence length of niobium are presented. Superconducting spin-valves and superconducting synapse, based on layered hybrid S/F nanostructures was designed and investigated. The layered nanostructures Nb/Co demonstrate change of the superconducting order parameter in thin s-films due to switching from the parallel to the antiparallel alignment of neighboring F-layers. We argue that such superlattices can be used as tunable kinetic inductors for ANN synapses design. |
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