Toward a self-driving ultrafast fiber laser

Mechanically Robust Biopolymer Optical Fibers with Enhanced Performance in the Near-Infrared Region

Polymer optical fibers (POFs) are lightweight, fatigue-tolerant, and suitable for local area networks, automobiles, aerospace, smart textiles, supercomputers, and servers. However, commercially available POFs are exclusively fabricated using synthetic polymers derived from nonrenewable resources. Recently, attempts have been made to fabricate biocompatible and biopolymeric optical fibers. However, their limitations in mechanical performance, thermal stability, and optical properties foil practical applications in waveguiding. Here, we report a comprehensive study of the preparation of biopolymer optical fibers with tailored mechanical strength, thermal properties, and their short-distance applications. Specifically, we use alginate as one of the key components with methylcelluloses to promote readily scalable wet spinning at ambient conditions to fabricate 21 combinations of composite fibers. The fibers display high maximum strain (up to 58%), Young's modulus (up to 11 GPa), modulus of toughness (up to 63 MJ/m3), and a high strength (up to 195 MPa), depending on the composition and fabrication conditions. The modulus of toughness is comparable to that of glass optical fibers, while the maximum strain is nearly 15 times higher. The mechanically robust fibers with high thermal stability allow rapid humidity, touch sensing, and complex shapes such as serpentine, coil, or twisted structures without losing their light transmission properties. More importantly, the fibers display enhanced optical performance and sensitivity in the near-infrared (NIR) region, making them suitable for advanced biomedical applications. Our work suggests that biobased materials offer innovative solutions to create short-distance optical fibers from fossil fuel-free resources with novel functionalities.

Group-velocity-locked vector solitons and dissipative solitons in a single fiber laser with net-anomalous dispersion

Laser cavities which can generate different types of ultrashort pulses are attractive for practical applications and the study of pulse dynamics. Here, we report the first experimental observation of both conventional solitons (CS) and dissipative solitons (DS) generated from a single all-fiber laser with net-anomalous dispersion. A birefringence-related intracavity Lyot filter with an adjustable extinction ratio enables the switching between the two types of ultrashort pulses. Depending on the polarization controller settings and the pump power, either chirp-free CS with a pulse energy of 406 pJ and a spectral bandwidth of 5.1 nm or up-chirped DS with a pulse energy of 5.1 nJ and an optical bandwidth of 9.6 nm can be generated. Similar polarization features are observed when the laser switches between different soliton operations as both CS and DS are group-velocity-locked vector solitons. Our work paves a novel way to generate dissipative solitons with a relatively high pulse energy (one order of magnitude larger than for CS) and a large chirp directly from an all-fiber net-anomalous-dispersion cavity through birefringent filter management.

Machine learning analysis of instabilities in noise-like pulse lasers

Neural networks have been recently shown to be highly effective in predicting time-domain properties of optical fiber instabilities based only on analyzing spectral intensity profiles. Specifically, from only spectral intensity data, a suitably trained neural network can predict temporal soliton characteristics in supercontinuum generation, as well as the presence of temporal peaks in modulation instability satisfying rogue wave criteria. Here, we extend these previous studies of machine learning prediction for single-pass fiber propagation instabilities to the more complex case of noise-like pulse dynamics in a dissipative soliton laser. Using numerical simulations of highly chaotic behaviour in a noise-like pulse laser operating around 1550 nm, we generate large ensembles of spectral and temporal data for different regimes of operation, from relatively narrowband laser spectra of 70 nm bandwidth at the -20 dB level, to broadband supercontinuum spectra spanning 200 nm at the -20 dB level and with dispersive wave and long wavelength Raman extension spanning from 1150-1700 nm. Using supervised learning techniques, a trained neural network is shown to be able to accurately correlate spectral intensity profiles with time-domain intensity peaks and to reproduce the associated temporal intensity probability distributions.

Inverse design of mode-locked fiber laser by particle swarm optimization algorithm

A wide variety of laser applications, that often require radiation with specific characteristics, and relative flexibility of laser configurations offer a prospect of designing systems with the parameters on demand. The inverse laser design problem is to find the system architecture that provides for the generation of the desired laser output. However, typically, such inverse problems for nonlinear systems are sensitive to the computation of the gradients of a target (fitness) function making direct back propagation approach challenging. We apply here particle swarm optimization algorithm that does not rely on the gradients of the fitness function to the design of a fiber 8-figure laser cavity. This technique allows us to determine the laser cavity architectures tailored to generating on demand pulses with duration in the range of 1.5-105 ps and spectral width in the interval 0.1-20.5 nm. The proposed design optimisation algorithm can be applied to a variety of laser applications, and, more generally, in a range of engineering systems with flexible adjustable configurations and the outputs on demand.

Predictions of resonant mode characteristics for terahertz quantum cascade lasers with distributed feedback utilizing machine learning

Terahertz quantum cascade lasers (THz QCLs) are the most powerful solid-state THz sources so far and THz QCLs with various distributed feedback (DFB) gratings have demonstrated single-mode emission, collimated beam, frequency tunability and high output power. Resonant mode characteristics of THz QCLs with DFB, including frequency, loss and electric-field distributions, are important for waveguide analysis, fabrication and indication of THz QCLs' radiative performance. Typically, predictions of these characteristics rely on numerical simulations. However, traditional numerical simulations demand a large amount of running time and computing resources, and have to deal with the trade-off between accuracy and efficiency. In this work, machine learning models are designed to predict resonant mode characteristics of THz QCLs with first-order, second-order, third-order DFB and antenna-feedback waveguides according to the four input structural parameters, i.e. grating period, total length of waveguide, duty cycle of grating and length of highly-doped contact layer. The machine learning models are composed of a multi-layer perceptron for predictions of frequency and loss, and an up-sampling convolutional neural network for predictions of electric-field distribution of the lowest-loss mode, respectively. A detailed study on more than 1000 samples shows high accuracy and efficiency of the proposed models, with Pearson correlation coefficients over 0.99 for predictions of lasing frequency and loss, median peak signal-to-noise ratios over 33.74dB for predictions of electric-field distribution, and the required time of prediction is within several seconds. Moreover, the designed models are widely applicable to various DFB structures for THz QCLs. Resonators with graded photonic heterostructures and novel phase-locked arrays are accurately predicted as examples.

Graphene Self-Phase-Lockers Formed around a Cu Wire Hub for Ring Resonators Incorporated into 57.8 Gigahertz Fiber Pulsed Lasers

We demonstrate graphene-functionalized self-phase-locking of laser pulses for a dramatically elevated repetition rate by employing an intrinsic resonating structure in a fiber ring laser cavity, the modes thereby satisfying the phase-matching condition passively, through both the resonator and the laser cavity. Graphene is directly synthesized around a 1-mm-diameter Cu wire catalyst, avoiding the deleterious transfer process. The wire provides a form factor to the fiber ring resonator as a versatile winding hub, guaranteeing damage-minimized and recyclable contact of the synthesized graphene with a diameter-controlled optical microfiber. In-depth analysis of the graphene confirms the optical nonlinearity critically required for pulse formation. The laser-graphene interaction, the intermode phase-locking function of graphene, and the pulse formation with the resonator are systematically elucidated to explain the experimentally generated laser pulses at a repetition rate of 57.8 gigahertz (GHz). Additionally, tunability of the repetition rate up to 1.5 GHz by the photothermal effect of graphene is demonstrated.

Smart lasers tame complex spatiotemporal cavity dynamics

By associating multimode fibers, optical wavefront manipulation, and a feedback loop controlled by a genetic algorithm, researchers have demonstrated that nonlinear spatiotemporal dynamics can be flexed within the laser cavity to achieve a user-specified objective, such as the lasing wavelength, output power, beam profile or pulsed operation.