Superconducting disordered neural networks for neuromorphic processing with fluxons

Uday S Goteti; Han Cai; Jay C LeFebvre; Shane A Cybart; Robert C Dynes

doi:10.1126/sciadv.abn4485

Superconducting disordered neural networks for neuromorphic processing with fluxons

Sci Adv. 2022 Apr 22;8(16):eabn4485. doi: 10.1126/sciadv.abn4485. Epub 2022 Apr 22.

Authors

Uday S Goteti^{1

2}, Han Cai², Jay C LeFebvre³, Shane A Cybart², Robert C Dynes¹

Affiliations

¹ Department of Physics, University of California, San Diego, CA 92093, USA.
² Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA.
³ Department of Physics, University of California, Riverside, CA 92521, USA.

Abstract

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 YBa₂Cu₃O_{7 - 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.