Real-time fully digital control scheme for pulse coherent beam combining.

A fully digital control scheme for non-polarization-maintaining (non-PM) nanosecond pulse coherent beam combining (CBC) is proposed, where digital locking of optical coherence by single-detector electronic-frequency tagging (LOCSET) for active phase control and stochastic parallel gradient descent (SPGD) for active polarization control is proposed. The fully digital control scheme is integrated on a real-time field-programmable gate array (FPGA) empowered hardware platform and then experimentally validated in a four-channel all-fiber non-fully polarization-maintaining nanosecond pulse CBC system. Consequently, the system can be fully locked in 9.5 ms, and the polarization extinction ratio (PER) of the combined beam is 21.5 dB with a CBC efficiency of 95.3%. The fully digital control scheme integrates the advantages of digital LOCSET and multi-channel active polarization control, enhancing the channel scalability and the potential output power of the non-PM pulse CBC system.

Data-driven inverse design of mode-locked fiber lasers.

The diverse applications of mode-locked fiber lasers (MLFLs) raise various demands on the output of the laser, including the pulse duration, energy, and shape. Simulation is an excellent method to guide the design and construction of an MLFL for on-demand laser output. Traditional simulation of an MLFL uses the split-step Fourier method (SSFM) to solve the nonlinear Schrödinger (NLS) equation, which suffers from high computational complexity. As a result, the inverse design of MLFLs via the traditional SSFM-based simulation method relies on the design experience. Here, a completely data-driven approach for the inverse design of MLFLs is proposed, which significantly reduces the computational complexity and achieves a fast automatic inverse design of MLFLs. We utilize a recurrent neural network to realize fast and accurate MLFL modeling, then the desired cavity settings meeting the output demands are searched via a deep-reinforcement learning algorithm. The results prove that the data-driven method enables the accurate inverse design of an MLFL to produce a preset target femtosecond pulse with a certain duration and pulse energy. In addition, the cavity settings generating soliton molecules with different target separations can also be located via the data-driven inverse design. With the GPU acceleration, the time consumption of the data-driven inverse design of an MLFL is less than 1.3 hours. The proposed data-driven approach is applicable to guide the inverse design of an MLFL to meet the different demands of various applications.

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method.

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and strong generalization ability with high accuracy, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be generalized accurately during the predicting process. The results represent a remarkable improvement in ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.

Neural network enabled time stretch spectral regression.

Spectral interferometry is utilized in a wide range of biomedical and scientific applications and metrology. Retrieving the magnitude and phase of the complex electric field from the interferogram is central to all its applications. We report a spectral interferometry system that utilizes a neural network to infer the magnitude and phase of femtosecond interferograms directly from the measured single-shot interference patterns and compare its performance with the widely used Hilbert transform. Our approach does not require apriori knowledge of the shear frequency, and achieves higher accuracy under our experimental conditions. To train the network, we introduce an experimental technique that generates a large number of femtosecond interferograms with known (labeled) phase and magnitude profiles. While the profiles for these pulses are digitally generated, they obey causality by satisfying the Kramer-Kronig relation. This technique is resilient against nonlinear optical distortions, quantization noise, and the sampling rate limit of the backend digitizer - valuable properties that relax instrument complexity and cost.

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.