  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.
