We propose and demonstrate the use of multimode fibers (MMF) inside a laser cavity as a new path to generate spatiotemporal modelocked pulses with high beam quality and high energy. Prior to our work, MMFs in optical cavities resulted in the generation of low-quality output beam profiles by spatiotemporal mode-locking. Here we present a versatile approach to reach high energy per pulse directly in the mode-locked MMF oscillator with a near single-mode output beam profile. Our approach relies on spatial beam self-cleaning via the nonlinear Kerr effect inside the cavity achieved by controlling spatiotemporal pulse propagation with a dispersion-managed design. We demonstrate the versatility of our approach with Yb-doped and Er-doped multimode laser cavities which generate pulse energies of 24 nJ and 16 nJ, respectively. The high peak power reached in the MMF within the cavity induced a Kerr self-beam cleaning which produced a near Gaussian mode output (M2<1.13).
KEYWORDS: Neural networks, Signal generators, Data modeling, Gold, Embedded systems, Diffusers, Detection and tracking algorithms, Control systems, Brain
We propose a general neural-network based learning framework to solve highly ill-posed problems to predict a system’s forward and backward response function. Such an approach has applications in target-oriented system’s control in fields such as, optics, neuroscience and robotics. The proposed method is able to find the appropriate continuous space input of a system that results in a desired output, despite the input-output relation being nonlinear, the system being time-variant and\or with incomplete measurements of the systems variables and lack of labeled data required for supervise learning.
We propose an imaging method for controlling the output of scattering media such as multimode fibers using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods.
The performance of fiber mode-locked lasers is limited due to the high nonlinearity induced by the spatial confinement of the single-mode fiber core. To massively increase the pulse energy of the femtosecond pulses, amplification is performed outside the oscillator. Recently, spatiotemporal mode-locking has been proposed as a new path to fiber lasers. However, the beam quality was highly multimode, and the calculated threshold pulse energy (>100 nJ) for nonlinear beam self-cleaning was challenging to realize. We present an approach to reach high energy per pulse directly in the mode-locked multimode fiber oscillator with a near single-mode output beam. Our approach relies on spatial beam self-cleaning via the nonlinear Kerr effect, and we demonstrate a multimode fiber oscillator with M2 < 1.13 beam profile, up to 24 nJ energy, and sub-100 fs compressed duration. Nonlinear beam self-cleaning is verified both numerically and experimentally for the first time in a mode-locked multimode laser cavity. The reported approach is further power scalable with larger core sized fibers up to a certain level of modal dispersion and could benefit applications that require high-power ultrashort lasers with commercially available optical fibers.
We present the first spatiotemporally mode-locked fiber laser with self-similar pulse evolution, to the best of our knowledge. Our multimode fiber laser produces amplifier similaritons with near-Gaussian beam quality (M2<1.4) at the output. Ytterbium based laser generates 2.3 ps pulses at 1030 nm with 2.4 nJ energy. The output pulses are externally compressed to 192 fs with a grating compressor. Intracavity large spectral breathing (>6) and less chirped pulses than the cavity induced total dispersion are the verifications of the spatiotemporal self-similar pulse propagation.
We propose a data-driven approach for light transmission control inside multimode fibers (MMFs). Specifically, we show that a convolutional neural network is able to reconstruct amplitude/phase modulated images from scrambled amplitude-only images obtained at the output of a 0.75m long MMF with a fidelity (correlation) as high as ~98%. We show that the trained network shows good generalization as well. In particular, it is shown that the network is able to reconstruct images that do not belong to train/test datasets.
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