Abstract
We report an ultracompact on-fiber multiparameter sensor that employs multiple guided-wave surface plasmon resonances (GWSPRs) and machine learning based signal processing. The sensor structure entails a gold plasmonic crystal cavity covered with a planar dielectric waveguide on the end-face of a single-mode fiber. Incident light on the gold gratings of the plasmonic crystal cavity excites surface plasmon resonances, which are coupled into the waveguide and induce multiple, high-Q GWSPRs. Due to the difference in the field distributions of these resonance modes, the resonance dips in the sensor reflection spectrum have distinctive responses to external stimuli, which enables simultaneous measurement of multiple parameters. To enhance the sensing accuracy, a neural network model is used to demodulate the sensor responses. A proof-of-concept sensor using a UV-curable polymer as a waveguide material is demonstrated for simultaneous measurement of temperature and salinity of salt solutions. Although we only demonstrate the measurement of two parameters, the proposed sensor has the capability to measure more parameters if a multilayer waveguide structure made of different functional materials is used. The proposed sensor can benefit multifunctional analysis in a broad range of biological, chemical, and environmental monitoring applications.
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