A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wideband transmission spectra that consist of multiple resonant modes. It is different from the previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

中文翻译：

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通过机器学习进行多模微谐振器传感的实验演示

实验证明了基于自干扰微环谐振器的多模微腔传感器。所提出的多模传感方法是通过记录由多个谐振模式组成的宽带传输光谱来实现的。它不同于以往的耗散传感方案，其目的是测量微腔内单个谐振模式的透射深度变化。在这里，通过结合耗散感知机制和机器学习算法，可以有效地融合从宽带频谱中提取的多模感知信息来估计目标参数。多模传感方法不受激光频率噪声和系统缺陷的影响，因此我们的工作向研究实验室外的微腔传感器实际应用迈出了一大步。调整施加在芯片上的微型加热器上的电压，以通过热光效应对透射率产生影响。作为原理验证实验，电压是通过多模传感方法检测的。实验结果表明，在大测量范围内，通用回归神经网络对多模传感的检测限降低到单模传感的6.7%。