As light propagates through a multimode fiber, the optical modes exchange power, create new optical frequencies via a complex spatiotemporal non-linear transformation that occurs at relatively low optical powers because of the light confinement and the long interaction length that is possible in a fiber. In this talk, we will review exciting new developments in this field including from our research group.
In particular, we show recent results of programming the nonlinear interaction in MMFs for machine learning applications. Several different databases were used to train a system consisting of a multimode fiber of different length/core size of a MMF and a simple, single layer digital network. In several recognition tasks, the classification accuracy that was obtained was comparable to deep, digitally implemented networks. The energy requirement for training and reading the optical system was orders of magnitude less than the digital counterpart.
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