Packaged WGM MBR sensor for high-performance temperature measurement using CNN-based multimode barcode images

Abstract

The whispering gallery mode (WGM) optical microresonator sensors are emerging as a promising platform for precise temperature measurements, driven by their excellent sensitivity, resolution and integration. Nevertheless, challenges endure regarding stability, single resonant mode tracking, and real-time monitoring. Here, we demonstrate a temperature measurement approach based on convolutional neural network (CNN), leveraging the recognition of multimode barcode images acquired from a WGM microbottle resonator (MBR) sensor with robust packaged microresonator-taper coupling structure (packaged-MTCS). Our work ensures not only a high sensitivity of −14.28 pm/℃ and remarkable resolution of 3.5 × 10−4 ℃ across a broad dynamic range of 96 ℃ but also fulfills the demands for real-time temperature measurement with an average detection accuracy of 96.85% and a speed of 0.68s per image. These results highlight the potential of high-performance WGM MBR sensors in various fields and lay the groundwork for stable soliton microcomb excitation through thermal tuning.