Polarization and wavelength routers based on diffractive neural network

Xiaohong Lin , Yulan Fu , Kuo Zhang , Xinping Zhang , Shuai Feng , Xiaoyong Hu

Front. Optoelectron. ›› 2024, Vol. 17 ›› Issue (3) : 22

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Front. Optoelectron. ›› 2024, Vol. 17 ›› Issue (3) : 22 DOI: 10.1007/s12200-024-00126-2
RESEARCH ARTICLE

Polarization and wavelength routers based on diffractive neural network

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Abstract

In the field of information processing, all-optical routers are significant for achieving high-speed, high-capacity signal processing and transmission. In this study, we developed three types of structurally simple and flexible routers using the deep diffractive neural network (D2NN), capable of routing incident light based on wavelength and polarization. First, we implemented a polarization router for routing two orthogonally polarized light beams. The second type is the wavelength router that can route light with wavelengths of 1550, 1300, and 1100 nm, demonstrating outstanding performance with insertion loss as low as 0.013 dB and an extinction ratio of up to 18.96 dB, while also maintaining excellent polarization preservation. The final router is the polarization-wavelength composite router, capable of routing six types of input light formed by pairwise combinations of three wavelengths (1550, 1300, and 1100 nm) and two orthogonal linearly polarized lights, thereby enhancing the information processing capability of the device. These devices feature compact structures, maintaining high contrast while exhibiting low loss and passive characteristics, making them suitable for integration into future optical components. This study introduces new avenues and methodologies to enhance performance and broaden the applications of future optical information processing systems.

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Optical diffractive neural network / All-optical routers / Polarization degree of freedom / Wavelength degree of freedom

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Xiaohong Lin,Yulan Fu,Kuo Zhang,Xinping Zhang,Shuai Feng,Xiaoyong Hu. Polarization and wavelength routers based on diffractive neural network. Front. Optoelectron., 2024, 17(3): 22 DOI:10.1007/s12200-024-00126-2

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