Electronic control of nonlinear-polarization-rotation mode locking in Yb-doped fiber lasers

Mode-locking and wavelength-tuning of a NPR fiber laser based on optical speckle

Passively mode-locked fiber lasers based on nonlinear polarization rotation (NPR) have been widely used due to their ability to produce short pulses with high peak power. Nevertheless, environmental perturbations can influence the mode-locked state, making it a challenge for the practical implementation. Therefore, researchers are searching for assessment criteria to quickly assist and maintain mode-locking of NPR fiber lasers. Speckle patterns containing spectral information can be generated when the laser transmits through a scattering medium, which can serve as indicators of the mode-locked state. The mode-locked regions are confined to the area close to the minimum texture contrast of speckle patterns. Based on these characteristics, we manually simulate the automatic mode-locking (AML). In addition, we utilize convolutional neural networks (CNNs) to recognize speckle patterns of wavelength tunable lasers and determine the center wavelength.

Autosetting soliton pulsation in a fiber laser by an improved depth-first search algorithm

Soliton pulsation is one of the most fascinating phenomena in ultrafast fiber lasers, owing to its rich nonlinear dynamics and potential generation of high peak power pulse. However, it is still a challenge to efficiently search for pulsating soliton in fiber lasers because it requires a fine setting of laser cavity parameters. Here, we report the autosetting soliton pulsation in a passively mode-locked fiber laser. The parameters of electronic polarization controller are intelligently adjusted to search for pulsating soliton state by the improved depth-first search algorithm. Moreover, the intensity modulation depth of pulsating soliton could be flexibly controlled. These findings indicate that the intelligent control of a fiber laser is an effective way to explore on-demand soliton dynamics and is also beneficial to the optimization of ultrafast laser performance.

Realization of carrier envelope phase control in an erbium-doped all-fiber comb via an intracavity electrical polarization controller

We present an erbium-doped all-fiber comb that is realized via intracavity piezoelectric mechanical responses by inserting an electrical polarization controller (EPC) for carrier envelope phase offset (f_ceo) control and sticking a piezoelectric transducer (PZT) for repetition rate (f_r) stabilization. With only three components inside the oscillator, it facilitates an all-fiber comb system with high integration and robustness. Besides f_ceo control, smart, recoverable, and precise searching of mode-locked states can also be achieved by finely tuning the EPC driving voltages. The f_ceo signal can be tightly phase-locked due to the 20 kHz 3 dB bandwidth of the EPC device. Compared with the traditional pump feedback mode, the EPC approach exhibits a larger and flatter gain in the near-zero-dispersion zone of the fiber comb, corresponding to less EPC induced intracavity fluctuations. It is also a benefit for reducing cross regulation between f_r and f_ceo in long-term stabilization of the comb.

Deep reinforcement learning for optical systems: A case study of mode-locked lasers

We demonstrate that deep reinforcement learning (deep RL) provides a highly effective strategy for the control and self-tuning of optical systems. Deep RL integrates the two leading machine learning architectures of deep neural networks and reinforcement learning to produce robust and stable learning for control. Deep RL is ideally suited for optical systems as the tuning and control relies on interactions with its environment with a goal-oriented objective to achieve optimal immediate or delayed rewards. This allows the optical system to recognize bi-stable structures and navigate, via trajectory planning, to optimally performing solutions, the first such algorithm demonstrated to do so in optical systems. We specifically demonstrate the deep RL architecture on a mode-locked laser, where robust self-tuning and control can be established through access of the deep RL agent to its waveplates and polarizers. We further integrate transfer learning to help the deep RL agent rapidly learn new parameter regimes and generalize its control authority. Additionally, the deep RL learning can be easily integrated with other control paradigms to provide a broad framework to control any optical system.

Harnessing a multi-dimensional fibre laser using genetic wavefront shaping

The multi-dimensional laser is a fascinating platform not only for the discovery and understanding of new higher-dimensional coherent lightwaves but also for the frontier study of the complex three-dimensional (3D) nonlinear dynamics and solitary waves widely involved in physics, chemistry, biology and materials science. Systemically controlling coherent lightwave oscillation in multi-dimensional lasers, however, is challenging and has largely been unexplored; yet, it is crucial for both designing 3D coherent light fields and unveiling any underlying nonlinear complexities. Here, for the first time, we genetically harness a multi-dimensional fibre laser using intracavity wavefront shaping technology such that versatile lasing characteristics can be manipulated. We demonstrate that the output power, mode profile, optical spectrum and mode-locking operation can be genetically optimized by appropriately designing the objective function of the genetic algorithm. It is anticipated that this genetic and systematic intracavity control technology for multi-dimensional lasers will be an important step for obtaining high-performance 3D lasing and presents many possibilities for exploring multi-dimensional nonlinear dynamics and solitary waves that may enable new applications.

Toward a self-driving ultrafast fiber laser

Femtosecond pulses from an ultrafast mode-locked fiber laser can be optimized in real time by combining single-shot spectral measurements with a smart genetic algorithm to actively control and drive the intracavity dynamics.

Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis

Mode-locked fiber lasers based on nonlinear polarization evolution can generate femtosecond pulses with different pulse widths and rich spectral distributions for versatile applications through polarization tuning. However, a precise and repeatable location of a specific pulsation regime is extremely challenging. Here, by using fast spectral analysis based on a time-stretched dispersion Fourier transform as the spectral discrimination criterion, along with an intelligent polarization search algorithm, for the first time, we achieved real-time control of the spectral width and shape of mode-locked femtosecond pulses; the spectral width can be tuned from 10 to 40 nm with a resolution of ~1.47 nm, and the spectral shape can be programmed to be hyperbolic secant or triangular. Furthermore, we reveal the complex, repeatable transition dynamics of the spectrum broadening of femtosecond pulses, including five middle phases, which provides deep insight into ultrashort pulse formation that cannot be observed with traditional mode-locked lasers.

Experimental study on polarization evolution locking in a stretched-pulse fiber laser

Polarization evolution locking (PEL) of a stretched-pulse fiber laser is experimentally investigated with a simple pulse selection method based on a fast electrooptic modulator, capable of revealing the temporal and spectrum evolution of the PEL pulses. Moreover, the wavelength dependence of PEL is observed by spectrally filtering the pulses and is further investigated for individual fiber laser comb lines through beat note measurements with narrow-linewidth cw lasers.

Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers

Polarization-based filtering in fiber lasers is well-known to enable spectral tunability and a wide range of dynamical operating states. This effect is rarely exploited in practical systems, however, because optimization of cavity parameters is nontrivial and evolves due to environmental sensitivity. Here, we report a genetic algorithm-based approach, utilizing electronic control of the cavity transfer function, to autonomously achieve broad wavelength tuning and the generation of Q-switched pulses with variable repetition rate and duration. The practicalities and limitations of simultaneous spectral and temporal self-tuning from a simple fiber laser are discussed, paving the way to on-demand laser properties through algorithmic control and machine learning schemes.

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 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.

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

When a laser is mode-locked, it emits a train of ultra-short pulses at a repetition rate determined by the laser cavity length. This article outlines a new and inexpensive procedure to force mode locking in a pre-adjusted nonlinear polarization rotation fiber laser. This procedure is based on the detection of a sudden change in the output polarization state when mode locking occurs. This change is used to command the alignment of the intra-cavity polarization controller in order to find mode-locking conditions. More specifically, the value of the first Stokes parameter varies when the angle of the polarization controller is swept and, moreover, it undergoes an abrupt variation when the laser enters the mode-locked state. Monitoring this abrupt variation provides a practical easy-to-detect signal that can be used to command the alignment of the polarization controller and drive the laser towards mode locking. This monitoring is achieved by feeding a small portion of the signal to a polarization analyzer measuring the first Stokes parameter. A sudden change in the read out of this parameter from the analyzer will occur when the laser enters the mode-locked state. At this moment, the required angle of the polarization controller is kept fixed. The alignment is completed. This procedure provides an alternate way to existing automating procedures that use equipment such as an optical spectrum analyzer, an RF spectrum analyzer, a photodiode connected to an electronic pulse-counter or a nonlinear detecting scheme based on two-photon absorption or second harmonic generation. It is suitable for lasers mode locked by nonlinear polarization rotation. It is relatively easy to implement, it requires inexpensive means, especially at a wavelength of 1550 nm, and it lowers the production and operation costs incurred in comparison to the above-mentioned techniques.

Automated mode locking in nonlinear polarization rotation fiber lasers by detection of a discontinuous jump in the polarization state

A strategy to align a mode-locked fiber laser with nonlinear polarization rotation is presented. This strategy is based on measurements of the output polarization state. It is shown that, as the angle of a motorized polarization controller inside the cavity is swept, the laser eventually reaches a mode-locked regime and the values of the Stokes parameters undergo an abrupt change. The sensing of this sudden variation is thus used to detect the mode-locking condition and a feedback mechanism drives the alignment of the polarization controller to force mode locking.

Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation

It has been observed that changes in the birefringence, which are difficult or impossible to directly measure, can significantly affect mode-locking in a fiber laser. In this work we develop techniques to estimate the effective birefringence by comparing a test measurement of a given objective function against a learned library. In particular, a toroidal search algorithm is applied to the laser cavity for various birefringence values by varying the waveplate and polarizer angles at incommensurate angular frequencies, thus producing a time-series of the objective function. The resulting time series, which is converted to a spectrogram and then dimensionally reduced with a singular value decomposition, is then labelled with the corresponding effective birefringence and concatenated into a library of modes. A sparse search algorithm (L(1)-norm optimization) is then applied to a test measurement in order to classify the birefringence of the fiber laser. Simulations show that the sparse search algorithm performs very well in recognizing cavity birefringence even in the presence of noise and/or noisy measurements. Once classified, the wave plates and polarizers can be adjusted using servo-control motors to the optimal positions obtained from the toroidal search. The result is an efficient, self-tuning laser.

High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm

We theoretically demonstrate that in a laser cavity mode-locked by nonlinear polarization rotation (NPR) using sets of waveplates and passive polarizer, the energy performance can be significantly increased by incorporating multiple NPR filters. The NPR filters are engineered so as to mitigate the multi-pulsing instability in the laser cavity which is responsible for limiting the single pulse per round trip energy in a myriad of mode-locked cavities. Engineering of the NPR filters for performance is accomplished by implementing a genetic algorithm that is capable of systematically identifying viable and optimal NPR settings in a vast parameter space. Our study shows that five NPR filters can increase the cavity energy by approximately a factor of five, with additional NPRs contributing little or no enhancements beyond this. With the advent and demonstration of electronic controls for waveplates and polarizers, the analysis suggests a general design and engineering principle that can potentially close the order of magnitude energy gap between fiber based mode-locked lasers and their solid state counterparts.