Modes classification in multimode optical fibers with a deep learning network

Abstract

Multimode optical fibers can support multiple guided modes for a given wavelength. Their number is determined by the optical frequency and the refractive index profile of investigated fiber. Each guided mode propagates in specific manner, which could be shown as its electromagnetic field distribution. Identification of linearly polarized (LP) modes is based on determining the number of extremes along two field cross-sections - radial and transversal. The field distribution of the same mode could be slightly different depending on wavelength and reflective index profile. To exhibit a dispersion characteristic for multimode fibers over a wide spectral range, a verification of refractive index value for a particular mode at a specific wavelength is needed. Manual verification of mode based on its field distribution is a time-consuming and error-prone process. To avoid these issues, we’ve used convolution neural networks (CNN) to accomplish the task.