Intelligent Breathing Soliton Generation in Ultrafast Fiber Lasers

Abstract

Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a specific mode-locked regime generally involves adjusting multiple control parameters, in connection with a wide range of accessible pulse dynamics. Machine-learning tools have recently shown promising for the design of smart lasers that can tune themselves to desired operating states. Yet, machine-learning algorithms are mainly designed to target regimes of parameter-invariant, stationary pulse generation, while the intelligent excitation of evolving pulse patterns in a laser remains largely unexplored. Breathing solitons exhibiting periodic oscillatory behavior, emerging as ubiquitous mode-locked regime of ultrafast fibre lasers, are attracting considerable interest by virtue of their connection with a range of important nonlinear dynamics, such as exceptional points, and the Fermi-Pasta-Ulam paradox. Here, we implement an evolutionary algorithm for the self-optimisation of the breather regime in a fibre laser modelocked through a four-parameter nonlinear polarisation evolution. Depending on the specifications of the merit function used for the optimisation procedure, various breathingsoliton states are obtained, including single breathers with controllable oscillation period and breathing ratio, and breather molecular complexes with a controllable number of elementary constituents. Our work opens up a novel avenue for exploration and optimisation of complex dynamics in nonlinear systems.

Farey tree and devil’s staircase of frequency-locked breathers in ultrafast lasers

“…The laser cavity used in this work has a nearly zero net dispersion. We have observed that frequency locking of breathers does not occur when the laser is operated at moderate or large normal dispersion 24 , 49 . Thus, a very small net cavity dispersion seems to be crucial to the emergence of frequency-locked breathers in a fibre laser.…”

“…In ref. 49 , we have introduced an approach based on an EA to search and control the breather mode-locking state in ultrafast fibre lasers. It relies on detecting and controlling the key parameter of breathers—the breathing frequency, to tune the period and breathing ratio of breathers.…”

“…Yet, the merit function of the breather mode locking used in ref. 49 is unable to distinguish between frequency-locked and unlocked breather states, where it usually breeds unlocked (unstable) states which have a wider parameter space. Here, we further develop our approach to directly pinpoint frequency-locked breathers so that the EA tunes the laser to these states only.…”

“…In the optimisation process, the RF spectrum is calculated through fast Fourier transform of the laser intensity recorded by the oscilloscope. The weighted sum of F ml and F b can be used to target the regime of breather mode locking 49 . The third term in Eq.…”

Fiber laser development enabled by machine learning: review and prospect

“…Most machine learning problems can transform into optimization ones in the end. Some researchers put several works with purely adaptive and robust optimization algorithms into the category of machine learning, for example, evolutionary algorithms, typically genetic algorithms, for coherent control of ultrafast dynamics [30], intelligent breathing soliton generation [31], and self-tuning mode-locked fiber lasers [32]. More common definitions of machine learning emphasize "learning" and "to gain knowledge" from data, and a classical one of them is "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E" [33].…”