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.

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.