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Area of Science:

  • Condensed Matter Physics
  • Neuromorphic Computing
  • Superconductivity

Background:

  • Magnetic fields in superconductors are quantized into discrete fluxons (flux quanta, Φ₀).
  • These fluxons are formed by microscopic circulating supercurrents.
  • Existing computing paradigms face limitations in energy efficiency and processing capabilities.

Purpose of the Study:

  • To introduce and experimentally demonstrate a novel multiterminal synapse network.
  • To explore brain-like spiking information flow using superconducting fluxon dynamics.
  • To establish a scalable neuromorphic computing architecture with attojoule-level energy dissipation.

Main Methods:

  • Designed a disordered array of superconducting loops with Josephson junctions.
  • Utilized YBa₂Cu₃O₇-δ based superconducting loops and Josephson junctions for experimental demonstration.
  • Investigated fluxon trapping, movement, and memory configuration within the network.

Main Results:

  • Demonstrated stable memory configurations based on trapped fluxons in superconducting loops.
  • Showcased fluxon flow through synaptic connections, influenced by input signals and electrical configuration.
  • Observed brain-like spiking information flow within the complex, reconfigurable energy landscape.

Conclusions:

  • The superconducting synapse network exhibits biologically similar architectural principles for neuromorphic computing.
  • The system allows for scalable computation with extremely low energy dissipation (attojoules per spike).
  • This work opens a new avenue for developing energy-efficient, brain-inspired computing systems.