Paper
22 November 2024 Forming diverse structured light arrays with general redefinable neural network
Hengyang Li, Jiaming Xu, Huaizhi Zhang, Xiaolong Liu, Yingxiong Qin
Author Affiliations +
Abstract
We propose a redefinable neural network (RediNet), realizing general modulation on diverse structured light arrays through a single approach. Exploiting the information sparsity of the array distribution, a redefinable dimension designation is used in RediNet, removing the burden of processing pixel-wise distributions. The prowess of originally generating arbitraryresolution holographs with fixed network is firstly demonstrated. The versatility is showcased in the generation of 2D/3D foci arrays, Bessel and Airy beams arrays, (perfect) vortex beam arrays, multi-channel compound vortex arrays and even snowflake-intensity arrays with arbitrarily-built phase functions. Considering the fine resolution, high speed, and unprecedented universality, RediNet can serve extensive applications such as next-generation optical communication, parallel laser direct writing, optical traps, and so on.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hengyang Li, Jiaming Xu, Huaizhi Zhang, Xiaolong Liu, and Yingxiong Qin "Forming diverse structured light arrays with general redefinable neural network", Proc. SPIE 13240, Holography, Diffractive Optics, and Applications XIV, 132400W (22 November 2024); https://doi.org/10.1117/12.3035291
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KEYWORDS
Structured light

Computer generated holography

Neural networks

Modulation

Airy beams

3D acquisition

Phase shift keying

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