AI‐Empowered Hyper‐Information Barcodes for Multiparameter Sensing With a Single Multimode Microresonator

ABSTRACT Accurate multiparameter sensing is crucial for characterizing and analyzing complex interactions in dynamic environments, providing deeper insights and enabling effective decision‐making across various fields. Conventional multiparameter sensors typically rely on multiple sensors or microstructures, each requiring separate calibration and complex data fusion to integrate the measurements from various sources. This not only increases operational complexity but may also introduce potential latency issues due to time delays in processing signals from different physical sensors, which can compromise the efficiency and accuracy of the system in certain applications. To address these limitations, we propose an innovative approach that utilizes multiple resonant modes with distinct spatially distributed sensing hotspots in a single optical microresonator to effectively mimic the functionalities of multiple sensors. Combined with machine learning algorithms, our multimode sensing system generates hyper‐information barcodes consisting of multiple modes that enable high‐precision multiparameter sensing and robust perturbation tracking. This AI‐empowered multimode approach demonstrates excellent robustness against signal down‐sampling and signal‐to‐noise ratio reduction, positioning the sensor as a promising calibration‐efficient and highly resilient platform for advanced optical sensing and analysis systems.