Random telegraph signal analysis with a recurrent neural network

N. J. Lambert, A. A. Esmail, M. Edwards, A. J. Ferguson, and H. G. L. Schwefel
Phys. Rev. E 102, 012312 – Published 21 July 2020
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Abstract

We use an artificial neural network to analyze asymmetric noisy random telegraph signals, and extract underlying transition rates. We demonstrate that a long short-term memory neural network can outperform other methods, particularly for noisy signals and measurements with limited bandwidths. Our technique gives reliable results as the signal-to-noise ratio approaches one, and over a wide range of underlying transition rates. We apply our method to random telegraph signals generated by quasiparticle poisoning in a superconducting double dot, allowing us to extend our measurement of quasiparticle dynamics to new temperature regimes.

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  • Received 13 February 2020
  • Accepted 30 June 2020

DOI:https://doi.org/10.1103/PhysRevE.102.012312

©2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsGeneral Physics

Authors & Affiliations

N. J. Lambert1,2,*, A. A. Esmail3, M. Edwards3, A. J. Ferguson3, and H. G. L. Schwefel1,2

  • 1Department of Physics, University of Otago, Dunedin 9016, New Zealand
  • 2The Dodd-Walls Centre for Photonic and Quantum Technologies, University of Otago, Dunedin 9016, New Zealand
  • 3Microelectronics Group, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, United Kingdom

  • *nicholas.lambert@otago.ac.nz

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Issue

Vol. 102, Iss. 1 — July 2020

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