Wideband and Low Insertion Loss Microwave Bandpass Filter Inverse Design Method Based on Convolutional Neural Network Fitting Model and Genetic Algorithm

To overcome the limitations of traditional bandpass filter design methods, which heavily rely on designer expertise and predefined topology, this paper proposes an inverse design methodology for microwave bandpass filter based on deep learning and genetic algorithms (GA). The filter structures are encoded as two-dimensional pixelated matrices, and a database is constructed through electromagnetic (EM) simulations. To reduce the computational cost of EM simulation, the dataset is further expanded using symmetry, mirroring, and rotation techniques. A convolutional neural network (CNN) is trained to model the mapping between S-parameters and arbitrary pixelated structural matrices, thereby broadening the design space. Subsequently, an improved GA is employed to synthesize filter structure that meets specific frequency response requirement. A filter operating within 0.5-2.08 GHz is successfully synthesized, fabricated, and measured. The experimental results demonstrate that the proposed filter achieves wide bandwidth, low insertion losses and good spurious rejection (20 dB @5.03f₀), benefiting from the removal of constraints imposed by predefined topology.