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Sensitivity and spectral control of network lasers

Author

Listed:
  • Dhruv Saxena

    (Imperial College London)

  • Alexis Arnaudon

    (Imperial College London
    Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech)

  • Oscar Cipolato

    (Imperial College London)

  • Michele Gaio

    (Imperial College London)

  • Alain Quentel

    (Imperial College London)

  • Sophia Yaliraki

    (Imperial College London)

  • Dario Pisignano

    (NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore
    Università di Pisa)

  • Andrea Camposeo

    (NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore)

  • Mauricio Barahona

    (Imperial College London)

  • Riccardo Sapienza

    (Imperial College London)

Abstract

Recently, random lasing in complex networks has shown efficient lasing over more than 50 localised modes, promoted by multiple scattering over the underlying graph. If controlled, these network lasers can lead to fast-switching multifunctional light sources with synthesised spectrum. Here, we observe both in experiment and theory high sensitivity of the network laser spectrum to the spatial shape of the pump profile, with some modes for example increasing in intensity by 280% when switching off 7% of the pump beam. We solve the nonlinear equations within the steady state ab-initio laser theory (SALT) approximation over a graph and we show selective lasing of around 90% of the strongest intensity modes, effectively programming the spectrum of the lasing networks. In our experiments with polymer networks, this high sensitivity enables control of the lasing spectrum through non-uniform pump patterns. We propose the underlying complexity of the network modes as the key element behind efficient spectral control opening the way for the development of optical devices with wide impact for on-chip photonics for communication, sensing, and computation.

Suggested Citation

  • Dhruv Saxena & Alexis Arnaudon & Oscar Cipolato & Michele Gaio & Alain Quentel & Sophia Yaliraki & Dario Pisignano & Andrea Camposeo & Mauricio Barahona & Riccardo Sapienza, 2022. "Sensitivity and spectral control of network lasers," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34073-3
    DOI: 10.1038/s41467-022-34073-3
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    References listed on IDEAS

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    1. Schaub, Michael T. & Lehmann, Jã–Rg & Yaliraki, Sophia N. & Barahona, Mauricio, 2014. "Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution," Network Science, Cambridge University Press, vol. 2(1), pages 66-89, April.
    2. Shi Gu & Fabio Pasqualetti & Matthew Cieslak & Qawi K. Telesford & Alfred B. Yu & Ari E. Kahn & John D. Medaglia & Jean M. Vettel & Michael B. Miller & Scott T. Grafton & Danielle S. Bassett, 2015. "Controllability of structural brain networks," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    3. Michael T. Schaub & Jean-Charles Delvenne & Renaud Lambiotte & Mauricio Barahona, 2019. "Multiscale dynamical embeddings of complex networks," LIDAM Reprints CORE 3037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Michele Gaio & Dhruv Saxena & Jacopo Bertolotti & Dario Pisignano & Andrea Camposeo & Riccardo Sapienza, 2019. "A nanophotonic laser on a graph," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
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