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Machine Learning Assisted Ultra-Wideband Fiber-Optics Mode Selective Coupler Design

We report a machine learning assisted design strategy for ultra-wideband fiber-optics mode selective coupler (MSC). With the help of deep neural network (DNN), we are able to efficiently obtain the complex mapping relationship between structure parameters of the bridge fiber and effective refractive index (RI) of LP01 mode over the S+C+L band. For high-order modes (HOMs) arising in the arbitrarily chosen few-mode fiber (FMF), the trained DNN can rapidly optimize the bridge fiber parameters. After optimization, the ultra-wideband phase matching between the effective RI of LP01 mode arising in the bridge fiber and the effective RI of specific mode arising in the FMF over the S+C+L band can be realized. As an example, ultra-wideband fiber-optics 4-LP MSC with an average mode conversion efficiency (MCE) of more than 93% and an average mode extinction ratio (MER) of more than 17 dB can be secured. In comparison with the traditional genetic algorithm (GA), the DNN-assisted fiber-optics MSC design can significantly reduce the optimization time from 4 hours to 45 seconds, together with the maintenance of excellent ultra-wideband performance. Meanwhile, the ultra-wideband MSC for orbital angular momentum (OAM) mode conversion is demonstrated. Both OAM+1 and OAM+2 modes with an average mode purity of more than 97% and an average MCE of more than 89% over the S+C+L band can be secured. We envision the DNN-assisted fiber-optics device enables potential applications for future mode division multiplexing and wavelength division multiplexing hybrid optical transmission system.