On-chip high-sensitivity photonic temperature sensor based on a GaAs microresonator

Machine learning-assisted high-accuracy and large dynamic range thermometer in high-Q microbubble resonators

Whispering gallery mode (WGM) resonators provide an important platform for fine measurement thanks to their small size, high sensitivity, and fast response time. Nevertheless, traditional methods focus on tracking single-mode changes for measurement, and a great deal of information from other resonances is ignored and wasted. Here, we demonstrate that the proposed multimode sensing contains more Fisher information than single mode tracking and has great potential to achieve better performance. Based on a microbubble resonator, a temperature detection system has been built to systematically investigate the proposed multimode sensing method. After the multimode spectral signals are collected by the automated experimental setup, a machine learning algorithm is used to predict the unknown temperature by taking full advantage of multiple resonances. The results show the average error of 3.8 × 10^-3°C within the range from 25.00°C to 40.00°C by employing a generalized regression neural network (GRNN). In addition, we have also discussed the influence of the consumed data resource on its predicted performance, such as the amount of training data and the case of different temperate ranges between the training and test data. With high accuracy and large dynamic range, this work paves the way for WGM resonator-based intelligent optical sensing.

On-chip simultaneous measurement of humidity and temperature using cascaded photonic crystal microring resonators with error correction

We present the design, fabrication, and characterization of cascaded silicon-on-insulator photonic crystal microring resonators (PhCMRRs) for dual-parameter sensing based on a multiple resonances multiple modes (MRMM) technique. Benefitting from the slow-light effect, the engineered PhCMRRs exhibit unique optical field distributions with different sensitivities via the excitation of dielectric and air modes. The multiple resonances of two distinct modes offer new possibilities for enriching the sensing receptors with additional information about environmental changes while preserving all essential properties of traditional microring resonator based sensors. As a proof of concept, we demonstrate the feasibility of extracting humidity and temperature responses simultaneously with a single spectrum measurement by employing polymethyl methacrylate as the hydrophilic coating, obtaining a relative humidity (RH) sensitivity of 3.36 pm/%RH, 5.57 pm/%RH and a temperature sensitivity of 85.9 pm/°C, 67.1 pm/°C for selected dielectric mode and air mode, respectively. Moreover, the MRMM enriched data further forges the capability to perform mutual cancellation of the measurement error, which improves the sensing performance reflected by the coefficient of determination (R²-value), calculated as 0.97 and 0.99 for RH and temperature sensing results, respectively.