在光学相位阵列中,通过温度变化形成适应性光束,并启用深度神经网络.
Optics express
|June 11, 2024
概括
深度神经网络 (DNN) 通过自适应性控制光学分相阵列 (OPA),以保持光束模式,尽管制造变化和温度变化. 这项技术可以为先进的光子应用提供精确的光束成型.
科学领域:
- 光子学是指光子学的使用方法.
- 光学工程是指光学工程.
- 机器学习 机器学习
背景情况:
- 集成光学相位阵列 (OPA) 由于频道不匹配而需要校准.
- OPA光束模式容易受到温度波动的扭曲.
研究的目的:
- 开发一个深度神经网络 (DNN),用于对OPA进行自适应控制.
- 为了弥补OPA光束成形过程中的工艺不匹配和温度变化.
主要方法:
- 实现了DNN来控制128通道OPA中的相调节器电压.
- 在OPA制造中使用商业光子 (SiP) 工艺.
- 经过证明的自适应光束形成和多峰光束生成.
主要成果:
- 在50度视野内实现了精确的光束形成.
- 证明了0.025°的精度,在固定温度下扫描0.1°的光束.
- 在20°C温度范围内保持光束完整性.
结论:
- 基于DNN的自适应控制有效地解决了OPA校准和温度稳定问题.
- 拟议的方法可以为集成光子系统提供强大而精确的光束转向.
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