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本文提出了一种基于人工神经网络(artificial neural network,ANN)增强的光子晶体光纤(photonic crystal fiber,PCF)表面等离子体共振(surface plasmon resonance,SPR)传感器，用于高灵敏度折射率测量。所提出的PCF结构通过在凹槽通道上沉积金/二氧化钛复合膜以增强倏逝场与分析物的相互作用，从而提升折射率灵敏度。在折射率1.33～1.40范围内，传感器的波长灵敏度为2 594 nm/RIU，相应的分辨率为3.86×10-5RIU，最大幅度灵敏度达1 492 RIU-1。更重要的是，构建了由3个隐藏层、每层50个神经元的ANN，用于预测PCF-SPR的损耗谱与幅度灵敏度。该模型在测试集和验证集上的均方误差分别为0.002 1和0.002 5。对训练集中未包括的结构参数■=60 nm的传感性能也能给出有效预测并得到验证，充分展示了ANN在光学传感器设计与优化方面的潜力。
Abstract：This work proposes an artificial neural network(ANN)-enhanced photonic crystal fiber(PCF) surface plasmon resonance(SPR) sensor for high-sensitivity refractive-index measurements. The PCF structure employs an Au/TiO2 composite film deposited on a grooved channel to strengthen the interaction between the evanescent field and the analyte, thereby improving RI sensitivity. Within the refractive-index range 1.33-1.40, the sensor attains a wavelength sensitivity of 2 594 nm/RIU, a corresponding resolution of 3.86×10-5 RIU, and a maximum amplitude sensitivity of 1492 RIU-1.Moreover, an ANN with three hidden layers and 50 neurons per layer is constructed to predict the loss spectra and amplitude sensitivity of the PCF-SPR device. The model achieves mean-squared errors of 0.002 1 and 0.002 5 on the test and validation sets, respectively.It also provides accurate predictions for an unseen configuration(■=60 nm),which were subsequently validated, demonstrating the potential of ANN for optical sensor design and optimization.
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基本信息:
DOI：10.13774/j.cnki.kjtb.2025.12.006
中图分类号:TP212;TP183
引用信息:
[1]王杰超,童清兰,梁艺,等.基于人工神经网络增强的PCF-SPR折射率传感器[J].科技通报,2025,41(12):53-59+109.DOI:10.13774/j.cnki.kjtb.2025.12.006.
基金信息:
福建省自然科学基金面上项目(2025J01353)