Athermal Microwave Photonic Sensor Based on Single Microring Resonance Assisted by Machine Learning

We propose a machine learning (ML) assisted athermal microwave photonic (MWP) sensing scheme with high resolution based on a single microring resonance. The immunity of temperature interference of the high-resolution sensing is achieved by employing MWP sideband processing based interrogation, and supervised machine learning based on support vector regression (SVR) and neural tangent kernel (NTK) that are effective on small datasets. The MWP sideband processing transforms the variation of the target measurand into the shift of an ultra-deep notch in the radio frequency (RF) spectrum relieving the fabrication requirements on the microresonators, while ML accurately predicts the measurand by using the modulator bias voltage or RF passband transmission together with the RF notch position. The proposed sensor is demonstrated for relative humidity (RH) measurements using a silicon-on-insulator microring coated with polymethyl methacrylate. About 50 dB high RF rejection ratio is achieved over the sensing process, indicating a high sensing resolution. Despite the small experimental datasets, the established SVR and NTK models consistently exhibit lower mean absolute errors (MAEs) than the linear regression model in the RH prediction in the presence of temperature drifts. The NTK models achieve the lowest MAEs of 1.01% RH and 1.03% RH when the RF passband transmission and modulator bias are selected as the model input, respectively. The equivalent performances of the RF passband transmission and modulator bias voltage further demonstrate the feasibility of athermal sensing based solely on the MWP interrogation results of a single microring resonance, which simplifies the design and reduces the complexity.

Automated summary

In this study, a machine learning assisted athermal microwave photonic sensing scheme based on a single microring resonance is proposed to achieve high resolution measurements. By employing MWP sideband processing and supervised machine learning based on SVR and NTK algorithms, the proposed sensor is capable of accurately predicting the measurand using modulator bias voltage or RF passband transmission. Experimental results demonstrate the feasibility and high performance of the proposed sensor in relative humidity measurements, achieving low mean absolute errors and high RF rejection ratio.