### 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/cm^{2} 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 language | English |
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Article number | 042029 |

Journal | Journal of Physics: Conference Series |

Volume | 507 |

Issue number | PART 4 |

DOIs | |

Publication status | Published - 2014 Jan 1 |

Event | 11th European Conference on Applied Superconductivity, EUCAS 2013 - Genoa, Italy Duration: 2013 Sep 15 → 2013 Sep 19 |

### ASJC Scopus subject areas

- Physics and Astronomy(all)

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## Cite this

*Journal of Physics: Conference Series*,

*507*(PART 4), [042029]. https://doi.org/10.1088/1742-6596/507/4/042029