Open AccessSuperconducting disordered neural networks for neuromorphic processing with fluxons
Author(s) -
Uday S. Goteti,
Han Cai,
Jay C. LeFebvre,
Shane A. Cybart,
R. C. Dynes
Publication year - 2022
Publication title -
science advances
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.928
H-Index - 146
ISSN - 2375-2548
DOI - 10.1126/sciadv.abn4485
Subject(s) - neuromorphic engineering , josephson effect , physics , fluxon , superconductivity , spiking neural network , artificial neural network , energy (signal processing) , condensed matter physics , topology (electrical circuits) , computer science , pi josephson junction , electrical engineering , quantum mechanics , artificial intelligence , engineering
In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ0 ), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa2 Cu3 O7 − δ -based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.