Experimental demonstration of multimode microresonator sensing by machine learning

TL;DR

This paper demonstrates a multimode microresonator sensor that uses machine learning to analyze broadband spectra, improving detection sensitivity and robustness for practical sensing applications.

Contribution

It introduces a novel multimode sensing method combining dissipative sensing and machine learning, enhancing detection accuracy and noise immunity over traditional single-mode approaches.

Findings

Detection limit reduced to 6.7% of single-mode sensing

Robust against laser frequency noise and system imperfections

Effective for practical microcavity sensing applications

Abstract

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.