Multimode optical fiber transmission with a deep learning network

Multimode fibers(MMFs)are an example of a highly scattering medium,which scramble the coherent light propagating within them to produce seemingly random patterns.Thus,for applications such as imaging and image projection through an MMF,careful measurements of the relationship between the inputs and outputs of the fiber are required.We show,as a proof of concept,that a deep neural network can learn the input-output relationship in a 0.75 m long MMF.Specifically,we demonstrate that a deep convolutional neural network(CNN)can learn the nonlinear relationships between the amplitude of the speckle pattern(phase information lost)obtained at the output of the fiber and the phase or the amplitude at the input of the fiber.Effectively,the network performs a nonlinear inversion task.We obtained image fidelities(correlations)as high as~98%for reconstruction and~94%for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix.We further show that the network can be trained for transfer learning,i.e.,it can transmit images through the MMF,which belongs to another class not used for training/testing.

Single-mode output by controlling the spatiotemporal nonlinearities in mode-locked femtosecond multimode fiber lasers

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 M^2<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.