Integrated Microresonator-Based Microwave Photonic Sensors Assisted by Machine Learning

Abstract

Microwave photonic (MWP) sensors, facilitating high speed and high resolution through the conversion of optical responses from the optical to the radio frequency domain, are becoming indispensable in an era where advanced sensing capabilities are paramount. The combination of machine learning (ML) with microwave photonics has provided advanced solutions that were previously unattainable. This paper begins by elucidating the operational principles of MWP sensing, and then proceeds to present the latest developments in the merging of ML and deep learning (DL) with integrated MWP sensors, where the development of photonic integration enables the realisation of on-chip sensors with significant improvements in both performance and miniaturization. ML/DL assisted MWP sensors with enhanced sensing capabilities, including athermal operation, resistance to noise and interference, multiple parameters detection, and extended sensing range, while exhibiting compactness, cost-effectiveness, and scalability, are presented. Prospective opportunities that could further propel the field of MWP sensing are also discussed.