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

R. I. Woodward, E. J. R. Kelleher: Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm. In: Scientific Reports, 6 , pp. 37616, 2016.

Abstract

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.

BibTeX (Download)

@article{Woodward_2016_ga,
title = {Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm},
author = {R. I. Woodward and E. J. R. Kelleher},
url = {http://www.riwoodward.com/publication_files/woodward_2016_ga.pdf},
doi = {10.1038/srep37616},
year  = {2016},
date = {2016-07-19},
journal = {Scientific Reports},
volume = {6},
pages = {37616},
abstract = {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.},
keywords = {fibre laser},
pubstate = {published},
tppubtype = {article}
}