Photonic micro-ring resonator design and analysis using machine learning techniques

Abstract

Micro-ring resonators (MRRs) have emerged as vital components in photonic applications, offering precise control of light at the nanoscale. Achieving optimal MRR design parameters is crucial for maximizing their performance in high-speed applications. This study aims to employ feature engineering and supervised machine learning (ML) techniques to comprehensively analyze MRRs. This includes impact of change in MRR design geometries, such as radius, coupling geometry, waveguide properties to key MRR output parameters, including the quality factor, full width at half maximum (FWHM), rise/fall time, and free spectral range (FSR). By utilizing results of over 1000 simulations in Lumerical, as well as incorporating the theoretical knowledge of MRRs, the study seeks to build highly accurate predictive model.