An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

T. Onomi, K. Nakajima

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

Original languageEnglish
Article number042029
JournalJournal of Physics: Conference Series
Volume507
Issue numberPART 4
DOIs
Publication statusPublished - 2014 Jan 1
Event11th European Conference on Applied Superconductivity, EUCAS 2013 - Genoa, Italy
Duration: 2013 Sep 152013 Sep 19

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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