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SCIENCE CHINA Information Sciences, Volume 63, Issue 6: 160404(2020) https://doi.org/10.1007/s11432-020-2883-0

Automatic mode-locking fiber lasers: progress and perspectives

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  • ReceivedFeb 6, 2020
  • AcceptedApr 20, 2020
  • PublishedMay 13, 2020

Abstract

Polarization control in nonlinear polarization rotation based mode-locked fiber lasers is a long-term challenge. Suffering from the polarization drifts induced by environmental disturbances, nonlinear polarization rotation based mode-locked fiber lasers is difficult in continuously operating under the desired pulsation regime thereby substantially hindering their utilizations. The appearance of automatic mode-locking techniques brings the light in addressing this challenge. Combining with various algorithms and electrical polarization control, automatic mode-locking techniques resolve the dilemma of nonlinear polarization rotation based mode-locked fiber lasers. We review the research progress of automatic mode-locking techniques in detail. Furthermore, we comment on the perspectives and potential applications of automatic mode-locking techniques.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61575122).


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  • Figure 1

    (Color online) (a) The experimental setup of automated characterization and alignment of an NPR based MLFL in [51]; (b) the design of the DOAP; (c) the scanning result of the pulsation regimes area on the Poincarésphere; (d) the scanning result of the mode-locking regime area after numerical filtering. Reprinted with permission from [51]@Copyright 2010 Springer Nature. (e) The experimental setup of the electronic control of NPR based Yb-doped fiber MLFL in [52]. Reprinted with permission from [52]@Copyright 2012 Optical Society of America.

  • Figure 2

    (Color online) (a) The experimental setup in [53]; (b) RF inter-mode beat spectrum of the most stable (left) and less stable (right) FML regime at around 8 MHz; (c) time diagram of the programmatically mode lock starting: pump power (P), the LC control voltage (V), relation between the magnitude of the peak of the RF inter-mode beat spectrum and the LC control voltage (dashed line). Reprinted with permission from [53]@Copyright 2013 Optical Society of America.

  • Figure 3

    (Color online) (a) The experimental setup in [55]; (b) the averaged first Stokes parameter acquired when the steady FML regime is reached under unperturbed case (red), perturbation before Erbium-doped fiber (blue) and perturbation after Erbium-doped fiber (black). Reprinted with permission from [55]@Copyright 2015 Optical Society of America.

  • Figure 4

    (Color online) (a) The experimental setup of the proposed self-tuning MLFL in [56]; (b) the pulse counter part consists of six counters; the populations of the FML regime of the laser at (c) 20$^\circ$C; (d) 30$^\circ$C; (e) 40$^\circ$C; (f) 50$^\circ$C. Reprinted with permission from [56]@Copyright 2017 IEEE.

  • Figure 5

    (Color online) (a) The experimental setup for programmable and fast-switchable passively HML fiber laser in [57]; (b) simplified traversal algorithm for AML; (c) FFT result of the second-order HML regime; (d) FFT result of the third-order HML regime. The temporal pulse train of (e) the FML regime, (f) the second-order HML regime and (g) the third-order HML regime. Reprinted with permission from [57]@Copyright 2018 Optical Society of America.

  • Figure 6

    (Color online) (a) The experimental setup for automatically generating NLPs or mode-locked pulses in a Yb-doped fiber laser in [58]; (b) for both the NLP and the FML regime, the theoretical (left) and experimental (right) quadratic relation between the intensity of 2PA signal and the average power. Reprinted with permission from [58]@Copyright 2018 IEEE.

  • Figure 7

    (Color online) (a) Configuration of the ring cavity laser system that contains multiple NPR sections (upper) in [59]@Copyright 2013 Optical Society of America. (b) The experimental setup for locking an NPR-based MLFL through an evolutionary algorithm in [60]; (c) convergence of the average (squared blue points) and best (red round points) fitness in the EA [60]; (d) the temporal pulse train (left) and the optical spectrum (right) of the FML regime found by the EA [60]. Reprinted with permission from [60]@Copyright 2015 Optical Society of America.

  • Figure 8

    (Color online) (a) The experimental setup of the propose in `smart laser' in [62]; (b) fitness map ($x$ and $y$ axes are QWP1 and QWP2 angle, respectively, swept through 180$^{\circ}$), the compound fitness function consists of three components; (c) convergence of fitness for single realization (left), convergence of maximal (middle) and average fitness values over ten realizations (right); the FML regime after four consecutive realizations of the GA: (d) temporal pulse train, (e) autocorrelation traces, (f) after the laser is mechanically perturbed, fitness evolution indicating the GA recovers optimum mode-locking. Reprinted with permission from [62]@Copyright 2016 Springer Nature.

  • Figure 9

    (Color online) (a) The experimental setup of the GA-based control of birefringent filtering for self-tuning, self-pulsing fiber laser in [63]; (b) wavelength self-tuning: visualization of spectra in different generations (left), fitness evolution (inset: obtained spectra by self-tuning indicating tuning range) (right); (c) maps of laser characteristics in relation to the QWP1 angle ($x$ axis) and the pump power ($y$ axis): central wavelength (left) and repetition rate (right); (d) maps of laser characteristics in relation to the QWP1 angle ($x$ axis) and the pump power ($y$ axis): pulse duration (left) and fitness score with targets: a central wavelength of 1550 nm and a repetition rate of 15 kHz (right), self-tuning characteristics, with targets the central wavelength of 1550.0 nm and the repetition rate of 15 kHz; (e) convergence of fitness for best fitness in each generation (left) and average fitness of each generation (right); (f) the optical spectrum (left) and the temporal pulse (right); (g) the RF spectrum (left), optimum achieved fitness when targeting variable central wavelengths under the fixed repetition rate of 15 kHz (right). Reprinted with permission from [63]@Copyright 2017 Optical Society of America.

  • Figure 10

    (Color online) (a) The experimental setup in [64]. (b) The FML regimes found through a full-range scan, demonstrating spectra for several operating points. The four control values are represented as spatial locations and the color of the marker. (c) A target ANDi spectrum (the black dashed) with a spectral width of 25 nm centered at 1043 nm and the spectrum found by the GA (the red solid) with this target. The relation between pulse width and the environmental temperature in (d) a standard no electrical-polarization-control oscillator and (e) an LC stabilized oscillator. Reprinted with permission from [64]@Copyright 2017 Optical Society of America.

  • Figure 11

    (Color online) (a) The experimental setup of the self-tuning fiber laser in [65]. (b) The oscilloscope trace (left) and the RF spectrum (right) of the FML regimes. (c) The distribution histogram of pulse amplitude jitter from a full polarization scan. (d) Multiple laser lines in the CW state appear in the spectral at large jitter values. (e) The laser operates in the FML regime at the lower end of jitter values. (f) The laser operates in the single CW state at the largest peak in the histogram (jitter values of 0.1), two objective optimization. Objective 1 is the pulse amplitude jitter and objective 2 is the absolute value of the difference between the measured wavelength with maximum intensity and the target wavelength. protectłinebreak (g) Distribution of various regimes on a map of objective values. (h) Operation regime meets objective 1, but not objective 2. (i) Operation regime meets both objective 1 and 2. (j) Operation regime meets objective 2, but not objective 1. Reprinted with permission from [65]@Copyright 2018 SPIE.

  • Figure 12

    (Color online) (a) Real-time intelligent MLFL in [66]; (b) operation regimes, from left to right respectively shows the FML, the second-order HML, the third-order HML, the QS, and the QML operation regimes; (c) comparisons over initial lock time, recovery time, and number of regimes between recent AML studies and our study; (d) schematic of human-like algorithm; (e) time consumption of the FML regime on initial mode-locking (the blue dashed line) and recovery (the red dashed line) over ten successive experiments; (f) a 15-day running record. Reprinted with permission from [66]@Copyright 2019 Optical Society of America.

  • Figure 13

    (Color online) (a) GA-based real-time automatic MLFL setup in [67]; (b) the flowchart of the proposed modified GA; (c) time consuming performance comparison between the ARS and the modified GA. Reprinted with permission from [67]@Copyright 2020 IEEE.

  • Figure 14

    (Color online) (a) Abstract model of NPR-based MLFL contains two quarter-wave plates ($\alpha_1$ and $\alpha_2$), one half-wave plate ($\alpha_3$), one passive polarizer ($\alpha_p$), gain and the birefringence parameter $K$ in [68]; (b) the objective function (the black solid), energy (the red dashed), and kurtosis (the blue dotted) as a function of half-wave plate angle $\alpha_3$; protectłinebreak (c) energy of the waveforms as a function of time for the cases shown in diamond, square, and triangle; (d) single-input, single-output ESC; the principle of ESC; (e) illumination of sinusoidal perturbation to the input $\hat{u}$ close to an optimal value $u^*$; protectłinebreak (f) the curves $\xi$ is given by the input and high-pass filtered outputs; (g) multi-parameter ESC with a varying birefringence. Reprinted with permission from [68]@Copyright 2013 IEEE.

  • Figure 15

    (Color online) (a) The simulation setup in [69]; (b) 2-torus of the half-wave plate angle $\alpha_3$ and the polarizer angle $\alpha_p$ with sample points shown (dots) at a sampling rate of 20 Hz (the red point: the global optimum); (c) the time-series of the corresponding objective function (the red point: the global optimum); (d) spectrogram of the time series; protectłinebreak (e) singular values obtained by SVD, the largest 15 singular values are plotted in red; (f) SVD modes of the largest 15 singular values; (g) illustration of training algorithm; (h) illustration of execution algorithm. Reprinted with permission from [69]@Copyright 2019 Optical Society of America.

  • Figure 16

    (Color online) Schematic of the self-tuning fiber laser in [70]. (a) The laser cavity; (b) the objective function is dividing the energy by the kurtosis of the Fourier spectrum of the waveform; (c) the toroidal search and sparse approximation; (d) the ESC. Reprinted with permission from [70]@Copyright 2014 IEEE.

  • Figure 17

    (Color online) (a) Schematic of the deep learning controller in [72]; (b) comparison between the true birefringence (the blue line) and the samples from the latent space of two dimensional VAE's; (c) performance of the system despite significant random changes in birefringence over time. Reprinted with permission from [72]@Copyright 2018 Optical Society of America.

  • Figure 18

    (Color online) (a) The time-stretch-assisted intelligent mode-locking fiber laser in [90]; (b) spectral width programming from 10 to 40 nm, showing the spectra (left) and autocorrelation traces (right); (c) spectral shape programming: the fitted hyperbolic secant spectrum (left), fitted triangular spectrum (right). Reprinted with permission from [90]@Copyright 2020 Springer Nature.

  • Figure 19

    (Color online) Ultrafast nonlinear dynamics inside MLFLs observation using the TS-DFT. (a) Transition from the CW state to a stable FML regime; (b) soliton explosions. Reprinted with permission from [95]@Copyright 2019 Springer Nature. (c) Formation of a soliton molecule [96]@Copyright 2017 AAAS. (d) Transition dynamics from a narrow-spectrum FML regime to a wide-spectrum FML regime [90]@Copyright 2020 Springer Nature. (e) Characterizing the buildup process of a soliton molecule [97]@Copyright 2019 American Physical Society. (f) Transition dynamics from the FML regime to the second-order HML regime based on an intelligent MLFL [98]@Copyright 2019 IEEE.

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