Deep Learning Regulation of the Fano Effect in Microring Resonators

This study focuses on optimizing the sensing performance of microring resonators by constructing a cavity array on a microcavity-coupled waveguide and regulating its Fano linearity through a deep neural network. A forward prediction method is introduced to efficiently simulate the impact of pore arrays on the microcavity's transmission spectrum. Leveraging convolutional neural networks and inverse design techniques, directional control over the transmission spectrum of microring resonators is successfully achieved. By analyzing the variations in transmission performance of porous microring resonators under different performance metrics, the influence of cavity array structures on transmission characteristics is thoroughly investigated. This work provides a robust framework for the design and optimization of microring resonators in integrated photonic applications.