Machine learning-enhanced on-chip micro-ring resonator platform for detection and recognition of low-concentration gas mixtures.

Micro-ring resonator (MRR) platforms based on silicon-on-insulator substrates have shown great potential for gas detection applications. However, challenges such as weak signal intensity and insufficient selectivity remain in the detection of low-concentration mixed gases. To overcome these limitations, this study proposes a machine learning-enhanced silicon nitride-based micro-ring resonator chip for the detection and recognition of methane (CH), carbon dioxide (CO), and hydrogen sulfide (HS) gas mixtures. By combining micro-ring resonator sensing data with machine learning models, the detection performance of the optical waveguide sensor was substantially improved. Experimental results show that the sensing chip can accurately identify CH, CO, and HS, with limits of detection (LODs) of 153 ppb, 184 ppb, and 83 ppb, respectively. With the aid of machine learning algorithms, the sensor achieves a classification accuracy of 91.4% in complex multi-component gas environments and can precisely determine methane concentration in unknown gas mixtures, with an average error of only 4.7%. This study not only provides an innovative solution for the detection of low-concentration gas mixtures but also demonstrates the broad application prospects of silicon photonics in the field of gas sensing.