AI-Powered Inverse Design of Ku-Band SIW Resonant Structures by Iterative Residual Correction Network

Inverse electromagnetic modeling has emerged as a powerful approach for designing complex microwave structures with high accuracy and efficiency. In this study, we propose an Iterative Residual Correction Network (IRC-Net) for the inverse design of Ku-band Substrate Integrated Waveguide (SIW) components based on multimode resonators. We use a multimode resonance structure to demonstrate that it is possible to control the resonances of the structure. Therefore, these structures can be used for resonant components and smart filter design. The proposed deep learning architecture leverages residual neural networks to overcome the limitations of traditional inverse design techniques, such as the Feedforward Inverse Model (FIM), offering improved generalization and prediction accuracy. The approach begins with a FIM to generate initial design estimates, followed by an iterative correction strategy inspired by the Hybrid Inverse-Forward Residual Refinement Network (HiFR\textsuperscript{2}-Net), which we call IRC-Net. Experiments demonstrate that the IRC-Net achieves substantial improvements in prediction accuracy compared to traditional single-stage networks, validated through statistical metrics, full-wave electromagnetic simulations, and measurements. To validate the proposed framework, we first design and fabricate a three-resonance SIW structure. Next, we apply the trained IRC-Net model to predict the geometry of a four-resonance structure based on its desired frequency response. Both designs are fabricated and tested, showing strong agreement between the simulated, predicted, and measured results, confirming the effectiveness and practicality of the proposed method.