Fiber Bragg grating sensors demodulated by a speckle silicon chip.

Abstract

Fiber Bragg gratings (FBGs) are widely used as sensors for temperature, strain, and vibration measurement. However, current FBG demodulation methods face issues with stability, size, and cost. In this study, we proposed a silicon-on-insulator (SOI) chip to demodulate FBGs based on random speckles. A 20-mm-long coiled multimode silicon waveguide was designed to generate the speckle pattern, which was then compressed into 8 single-mode outputs. The architecture similarity between the convolutional neural network (CNN), and the proposed SOI chip was discussed. A multilayer perceptron (MLP) network was applied to regress the speckle data for prediction. The demonstrated experiments indicated that a standard deviation of 0.0414°C was achieved in the single FBG demodulation. Furthermore, we also explored the capability of demodulating multiple FBGs. This speckle-based SOI chip provides a highly stable, compact, and lightweight solution in a FBG sensing system.