论文标题
通过机器学习的多模微波传感的实验证明
Experimental demonstration of multimode microresonator sensing by machine learning
论文作者
论文摘要
通过实验证明了基于自我干扰微孔谐振器的多模微量腔传感器。提出的多模传感方法是通过记录由多种谐振模式组成的宽带传输光谱来实现的。它与以前的耗散传感方案不同，该方案旨在测量微腔中单个谐振模式的传输深度变化。在这里，通过结合耗散感应机制和机器学习算法，可以有效融合从宽带光谱中提取的多模传感信息以估计目标参数。多模传感方法不受激光频率噪声的免疫力和防御系统不完美的鲁棒性，因此我们的工作迈出了迈向研究实验室外微博传感器的实际应用迈出的重要一步。调整了芯片上微调器上施加的电压，以通过热光效应对透射率产生影响。作为原则实验，通过多模传感方法检测到电压。实验结果表明，通用回归神经网络对多模传感的检测极限降低到较大测量范围内单模传感的6.7％。
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.
