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All-Josephson junction logic cells and bio-inspired neuron based on 0−0−π junction inductorless blocks

Author

Listed:
  • Maksimovskaya, Anastasia A.
  • Ruzhickiy, Vsevolod I.
  • Klenov, Nikolay V.
  • Schegolev, Andrey E.
  • Bakurskiy, Sergey V.
  • Soloviev, Igor I.
  • Yakovlev, Dmitry S.

Abstract

Representing information as magnetic flux in superconducting circuits has enabled the development of fast and energy-efficient post-Moore digital circuits. Similar Josephson schemes have also been used to implement promising spiking neural networks. The primary limitation to the practical use of such devices is the relatively low integration density, mainly due to the presence of inductive elements in the circuit. Recently, a solution has been proposed to overcome this problem in circuits by completely removing the inductances and replacing them with conventional Josephson junctions and junctions with π-shifted current-phase relation (π-junctions). In this article, we consider the origin of the required bistability in circuits containing 0- and π-junctions. Furthermore, we extend the application of the all-Josephson junction (all-JJ) circuit approach to the realization of bio-inspired neuromorphic cells, using a superconducting neuron as an example. We claim that the 0-0-π inductorless block of Josephson junctions is a fundamental element for all-JJ logic and neuromorphic cells.

Suggested Citation

  • Maksimovskaya, Anastasia A. & Ruzhickiy, Vsevolod I. & Klenov, Nikolay V. & Schegolev, Andrey E. & Bakurskiy, Sergey V. & Soloviev, Igor I. & Yakovlev, Dmitry S., 2025. "All-Josephson junction logic cells and bio-inspired neuron based on 0−0−π junction inductorless blocks," Chaos, Solitons & Fractals, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:chsofr:v:193:y:2025:i:c:s0960077925000876
    DOI: 10.1016/j.chaos.2025.116074
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    References listed on IDEAS

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    1. Jens E. Pedersen & Steven Abreu & Matthias Jobst & Gregor Lenz & Vittorio Fra & Felix Christian Bauer & Dylan Richard Muir & Peng Zhou & Bernhard Vogginger & Kade Heckel & Gianvito Urgese & Sadasivan , 2024. "Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. J. Feldmann & N. Youngblood & C. D. Wright & H. Bhaskaran & W. H. P. Pernice, 2019. "All-optical spiking neurosynaptic networks with self-learning capabilities," Nature, Nature, vol. 569(7755), pages 208-214, May.
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