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使用生成对抗网络集成的等离子传感器.

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    这项研究将机器学习 (ML) 与光子晶体纤维 (PCF) 等离子传感器集成,以提高性能. 新型传感器设计实现了高灵敏度和分辨率的折射率传感,适用于化学和医学诊断.

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    科学领域:

    • 光子学是指光子学的使用方法.
    • 塑制剂是一种塑制剂.
    • 传感技术 传感技术
    • 机器学习 机器学习

    背景情况:

    • 机器学习 (ML) 在不同领域显著增强传感技术.
    • 光子晶体纤维 (PCF) 为先进的传感应用提供了独特的结构.
    • 等离子体传感利用表面等离子体共振进行高灵敏度检测.

    研究的目的:

    • 将ML技术与基于PCF的等离子传感器集成,以提高传感器性能.
    • 研究一种新的PCF结构,用于增强模式合和分析剂相互作用的开放通道.
    • 开发一种由生成对抗网络 (GANs) 增强的ML模型,用于准确的限制损失预测.

    主要方法:

    • 使用具有两个开放通道的PCF来促进分析物相互作用和模式合.
    • 在PCF通道中加入一层薄金层,以产生表面等离子体.
    • 使用生成对抗网络 (GAN) 来增强人工神经网络 (ANN) 模型的训练数据.

    主要成果:

    • 达到最大波长灵敏度为9000nm/RIU,振幅灵敏度为490.41RIU-1.
    • 显示高分辨率为1.11×10−5 RIU,最大功率 (FOM) 为138.04 RIU−1.
    • 通过GAN增强的ML模型准确地预测了各种分析物和波长的限制损失.

    结论:

    • 集成的ML和PCF等离子体传感器为折射率传感提供了卓越的性能.
    • 传感器的设计和ML方法可以在RI范围1.33到1.40内精确检测.
    • 这种多功能传感平台适用于化学传感和医学诊断的关键应用.