Machine learning based classification of higher-order spatial modes

Abstract

Aspects of the present disclosure describe systems, methods and structures for classification of higher-order spatial modes using machine learning systems and methods in which the classification of high-order spatial modes emitted from a multimode optical fiber does not require indirect measurement of the complex amplitude of a light beam's electric field using interferometry or, holographic techniques via unconventional optical devices/elements, which have prohibitive cost and efficacy; classification of high-order spatial modes emitted from a multimode optical fiber is not dependent on a light beam's alignment, size, wave front (e.g. curvature, etc.), polarization, or wavelength, which has prohibitive cost and efficacy; classification of higher-order spatial modes from a multimode optical fiber does not require a prohibitive amount of experimentally generated training examples, which, in turn, has prohibitive efficacy; and the light beam from a multimode optical fiber can be advantageously separated into two orthogonal polarization components, such that, the different linear combination of higher order spatial modes comprising each polarization component can be classified.