Experimental Demonstration of Multimode Microresonator Sensing by Machine Learning

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wide band transmission spectra that consist of multiple resonant modes. It is different from 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.

Automated summary

This study demonstrates a multimode microcavity sensor based on a self-interference microring resonator, which extracts multimode sensing information by recording wide band transmission spectra. By effectively combining the dissipative sensing mechanism and machine learning algorithm, it achieves estimation of target parameters and is robust against laser frequency noises and system imperfections.