Machine-learning-assisted orbital angular momentum recognition using nanostructures

Abstract

The recognition of orbital angular momentum (OAM) in light beams holds significant importance in optical communication. The majority of current OAM recognition techniques are highly sensitive to stringent alignment issues. The speckle-based OAM recognition method reported in [J. Opt. Soc. Am. A 39, 759 (2022)] is alignment-free in the transverse direction of light propagation and has been shown to operate successfully in the far-field region using macrostructures. This study introduces a proof-of-concept for speckle-learned OAM recognition with nanostructures, relaxing the strict alignment requirements in both the transverse and along the direction of light propagation. When the OAM beam interacts with random inhomogeneities at micron and/or nanoscale, it generates an OAM speckle field. Initially, a comprehensive examination of the dynamic evolution of OAM speckle fields, ranging from near field to far field, has been conducted using a ground glass diffuser, featuring random phase inhomogeneities at the micron scale. Subsequently, the investigation proceeds to randomly grown ZnO nanosheets on an aluminum substrate. To achieve rapid and precise OAM recognition, a tailored 3-layer CNN is trained and tested on OAM speckle fields ranging from near-field to far-field to attain an accuracy surpassing 92% . This research expands the technique's applicability, enabling recognition of OAM across near-field to far-field regimes, while leveraging micro-to nanostructures.