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463 27 |
Ultima descărcare din IBN: 2024-05-16 17:44 |
SM ISO690:2012 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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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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