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Phys. Rev. Applied 17, 054037 (2022) - Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons

Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons

R. Mirek, A. Opala, M. Furman, M. Król, K. Tyszka, B. Seredyński, W. Pacuski, J. Suffczyński, J. Szczytko, M. Matuszewski, and B. Piętka
Phys. Rev. Applied 17, 054037 – Published 23 May 2022

Abstract

We demonstrate that time-delayed nonlinear effects in exciton polaritons can be used to construct neural networks where information is coded in optical pulses arriving consecutively on the sample. The highly nonlinear effects are induced by time-dependent interactions with the excitonic reservoir. These nonlinearities allow the creation of a nonlinear XOR logic gate that can perform operations on the picosecond timescale. An optoelectronic neural network based on the constructed logic gate performs the classification of spoken digits with a high accuracy rate.

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  • Received 31 December 2021
  • Accepted 2 May 2022

DOI:https://doi.org/10.1103/PhysRevApplied.17.054037

© 2022 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

R. Mirek1, A. Opala2, M. Furman1, M. Król1, K. Tyszka1, B. Seredyński1, W. Pacuski1, J. Suffczyński1, J. Szczytko1, M. Matuszewski2, and B. Piętka1,*

  • 1Institute of Experimental Physics, Faculty of Physics, University of Warsaw, ul. Pasteura 5, Warsaw PL-02-093, Poland
  • 2The Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, Warsaw PL-02-668, Poland

  • *barbara.pietka@fuw.edu.pl

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Vol. 17, Iss. 5 — May 2022

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