Non-orthogonal optical multiplexing empowered by deep learning.

Orthogonality among channels is a canonical basis for optical multiplexing featured with division multiplexing, which substantially reduce the complexity of signal post-processing in demultiplexing. However, it inevitably imposes an upper limit of capacity for multiplexing. Herein, we report on non-orthogonal optical multiplexing over a multimode fiber (MMF) leveraged by a deep neural network, termed speckle light field retrieval network (SLRnet), where it can learn the complicated mapping relation between multiple non-orthogonal input light field encoded with information and their corresponding single intensity output. As a proof-of-principle experimental demonstration, it is shown that the SLRnet can effectively solve the ill-posed problem of non-orthogonal optical multiplexing over an MMF, where multiple non-orthogonal input signals mediated by the same polarization, wavelength and spatial position can be explicitly retrieved utilizing a single-shot speckle output with fidelity as high as ~ 98%. Our results resemble an important step for harnessing non-orthogonal channels for high capacity optical multiplexing.

Inverse design of coupled subwavelength dielectric resonators with targeted eigenfrequency and Q factor utilizing deep learning.

Subwavelength all-dielectric resonators supporting Mie resonances are promising building blocks in nanophotonics. The coupling of dielectric resonators facilitates advanced shaping of Mie resonances. However, coupled dielectric resonators with anisotropic geometry can only be designed by time-consuming simulation utilizing parameter scanning, hampering their applications in nanophotonics. Herein, we propose and demonstrate that a combination of two fully connected networks can effectively design coupled dielectric resonators with targeted eigenfrequency and Q factor. Typical examples are given for validating the proposed network, where the normalized deviation rates of eigenfrequency and Q factor are 0.39% and 1.29%, respectively. The proposed neutral network might become a useful tool in designing coupled dielectric resonators and beyond.