High-precision ethanol concentration microsensor with global spectra aided by the multi-layer perceptron

Abstract

Whispering gallery mode (WGM) resonators can be used for precision measurement thanks to their high sensitivity, small size, and fast response time. Nevertheless, the design of such sensors is usually achieved by selecting a typical single-mode tracking method, which leads to low utilization of a great deal of information in the resonance spectrum and affects the precision. Here, we use the multi-layer perceptron (MLP) deep learning algorithm to train the global spectra and realize the high-precision measurement of ethanol concentration. Firstly, a large number of transmission spectra of different ethanol concentrations are collected and directly used as the original data sets. Secondly, the MLP algorithm is used for training and testing. Finally, the local feature dimension is extracted from the global features of the spectrum for prediction. The results show that the prediction accuracy of the global spectra sensing is 99.81%, which is 13.02% higher than that of extracting 10 local features. In addition, the prediction accuracy of the MLP is compared with four other commonly used machine learning (ML) algorithms, and the results show that the MLP algorithm has the highest prediction accuracy. Therefore, the high-precision ethanol concentration sensor proposed in this paper opens a new way for intelligent optical micro-resonator sensing.