通过空心多模光纤通过神经网络模型的需求轨道角动量模式
Optics express
|February 18, 2026
概括
我们开发了一个机器学习模型来控制轨道角动量 (OAM) 束在专门的光纤中. 这一突破可以为先进的光操纵应用程序实现动态光束成型.
科学领域:
- 光子学是指光子学的使用方法.
- 光学通信是指光学通信.
- 机器学习 机器学习
背景情况:
- 轨道角动量 (OAM) 束为光通信提供了独特的特性.
- 在多模纤维中控制OAM模式是一项挑战.
- 抑制合空心光子晶体纤维 (IC-HCPCF) 支持大量的模式.
研究的目的:
- 介绍一种机器学习方法来生成和重新配置OAM束.
- 为了证明IC-HCPCF在结构化轻型运输方面的能力.
- 通过人工智能实现按需的光束成型.
主要方法:
- 训练基于神经网络的数字双胞胎用于139微米核心IC-HCPCF.
- 在绿色光谱范围内指导60多个LP类模式的偏振.
- 通过实验强度模式比较验证神经网络的准确性.
主要成果:
- 在实验和预测输出强度模式之间实现了高保真度.
- 皮尔森相关系数的中位数超过了98%,证实了模型的准确性.
- 在IC-HCPCF中证明了OAM光束的动态重新配置.
结论:
- 多模式IC-HCPCF是结构光的多功能平台.
- 机器学习为OAM光束控制提供了一种有效的方法.
- 这种方法可以实现按需的动态光束成型功能.
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