EM Topology Modeling for Waveguide Filter Based on CNN

This paper reviews and condenses the work originally presented in the paper 'A Cross-Channel Parametric CNN Framework for Microwave Topology Modeling' [1]. It presents a novel convolutional neural network (CNN) framework, termed Cross-Channel Parametric Small-Convolution Network (CCP-SConvNet), for efficient and accurate modeling of microwave topology components. The framework addresses key challenges in electromagnetic (EM) topology design, namely the highdimensional design space and computational cost of full-wave simulations. CCP-SConvNet incorporates a cross-channel parametric pooling mechanism that preserves spatial resolution while enabling adaptive feature fusion, alongside stacked small convolutional filters for fine-grained local feature extraction and parameter efficiency. To ensure the physical manufacturability of learned topologies, a modified breadth-first search (BFS) algorithm is integrated to enforce connectivity constraints, eliminating isolated metal patterns. The proposed method is validated through the modeling of a single-pole waveguide filter. Results show that CCP-SConvNet achieves a prediction error of 3.56% for the S-parameter response, reduces required training data by filtering non-manufacturable samples, and accelerates simulation from minutes to milliseconds. A filter prototype was fabricated and measured, showing good agreement with predictions, confirming the model's accuracy and practical feasibility.