矢量束在可见光谱中对120种模式进行分类.
Qi Jia1, Yanxia Zhang1, Bojian Shi1
1Institute of Advanced Photonics, School of Physics, Harbin Institute of Technology, Harbin 150001, China.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
研究人员开发了一种新的衍射深度神经网络 (D2NN) 束分类器. 这个设备有效地通过分极,角和半径指数同时对矢量束进行排序.
科学领域:
- 光学和光子学 在光学和光子学.
- 在光学领域的人工智能.
背景情况:
- 矢量旋转束具有三个自由度:极化 (P),角度指数 (l) 和半径指数 (p).
- 建立了单个自由度的高效分类,但同时分类仍然是一个重大挑战.
- 应用范围包括光学操纵,通信和成像.
研究的目的:
- 提出并实验证明一种紧而高效的束分类器,能够同时处理矢量束的三个自由度.
- 为了利用衍射深度神经网络 (D2NNs) 进行先进的光场操纵.
主要方法:
- 基于衍射深度神经网络 (D2NN) 的新型光束分类器的开发.
- 在可见光谱中,D2NN光束分类器的实验演示.
- 使用DNN对基于偏振,角和半径指数的矢量束进行排序.
主要成果:
- 120种不同的Laguerre-Gaussian (LG) 模式的成功和强大的同时分类.
- 展示DNN对复杂光场分类的能力.
- 在可见光光谱中验证拟议的方法.
结论:
- 拟议的D2NN基光束分类器提供了一个紧而高效的解决方案,用于在矢量束中同时分类多个自由度.
- 衍射深度神经网络显示出高级光场操纵的巨大潜力.
- 这项技术可以增强矢量束的各种应用.
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