Machine Learning-Driven Analysis and Optimization of Optical Ring Resonator for Integrated Photonic Applications

Ring resonators are pivotal components in photonics, enabling light manipulation for applications in telecommunications, optical sensing, and quantum computing. Integrating machine learning with photonic design opens new avenues for enhancing the performance and efficiency of these devices. In this study, we leverage the Lumerical software to create a comprehensive database of ring resonator designs and their performance metrics. Utilizing the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{k}$</tex>-Nearest Neighbors (KNN) regression algorithm, we develop a predictive model to optimize resonator parameters for specific applications. This approach allows for rapid exploration of the design space, uncovering innovative resonator configurations and improving sensitivity and efficiency. The proposed method demonstrates the potential of machine learning to accelerate photonic device development, offering a systematic, data-driven strategy for advancing ring resonator technology. Obtained accuracy of 82.8