Modal decomposition (MD) has become an indispensable analysis approach for revealing the modal characteristics of optical fibers. A new MD approach based on the convolutional neural network (CNN) is presented to retrieve the exact superposition of eigenmodes of few-mode fibers. Using the near-field beam intensity and phase patterns obtained from digital holography, not only the amplitude of each eigenmode but also the exact phase difference between the higher-order modes and the fundamental mode can be predicted. Numerical simulations validate the reliability and feasibility of the approach. When ten modes in the few-mode fiber are considered, the similarities of the intensity and phase pattern between the reconstructed fields and the given fields can achieve to 97.0% and 85.6%, respectively.

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Abstract. S-shaped bend waveguides are indispensable in planar light circuits to realize the lateral displacements and connections. High-density integrated optical circuits require S-shaped bend waveguides to be low loss and compact sized. In this paper, the loss mechanism of the S-shaped bend is briefly analyzed first, and then a novel scheme to optimize the waveguide bends is proposed. Using a modified random direction method, the random orthogonal axial gradient method (ROAGM), the configuration curves of the S-shaped bends fitted by polynomials are optimized. Numerical results show that the insertion losses of the optimized configuration curves are much smaller than those of the conventional cosine functions when the space requirement is strict.

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