Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.

ABSTRACT: Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of

SUBMITTER: Fang X 

PROVIDER: S-EPMC11251042 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature