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Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm

Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm...

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Detalles Bibliográficos
Autores principales: Woodward, R. I., Kelleher, E. J. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5116642/
https://www.ncbi.nlm.nih.gov/pubmed/27869193
http://dx.doi.org/10.1038/srep37616
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author Woodward, R. I.
Kelleher, E. J. R.
author_facet Woodward, R. I.
Kelleher, E. J. R.
author_sort Woodward, R. I.
collection PubMed
description Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm to intelligently locate optimum parameters for stable single-pulse mode- locking in a Figure-8 fibre laser, and fully automate the system turn-on procedure. Stable ultrashort pulses are repeatably achieved by employing a compound fitness function that monitors both temporal and spectral output properties of the laser. Our method of encoding photonics expertise into an algorithm and applying machine-learning principles paves the way to self-optimising ‘smart’ optical technologies.
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spelling pubmed-51166422016-11-28 Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm Woodward, R. I. Kelleher, E. J. R. Sci Rep Article Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm to intelligently locate optimum parameters for stable single-pulse mode- locking in a Figure-8 fibre laser, and fully automate the system turn-on procedure. Stable ultrashort pulses are repeatably achieved by employing a compound fitness function that monitors both temporal and spectral output properties of the laser. Our method of encoding photonics expertise into an algorithm and applying machine-learning principles paves the way to self-optimising ‘smart’ optical technologies. Nature Publishing Group 2016-11-21 /pmc/articles/PMC5116642/ /pubmed/27869193 http://dx.doi.org/10.1038/srep37616 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Woodward, R. I.
Kelleher, E. J. R.
Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title_full Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title_fullStr Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title_full_unstemmed Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title_short Towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
title_sort towards ‘smart lasers’: self-optimisation of an ultrafast pulse source using a genetic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5116642/
https://www.ncbi.nlm.nih.gov/pubmed/27869193
http://dx.doi.org/10.1038/srep37616
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