Inverse Design of Ultra-Wideband Microstrip Filters Based on Conditional Diffusion Networks

Traditional design methods for ultra-wideband (UWB) filters rely on complex electromagnetic (EM) simulations and iterative parameter optimization, consuming significant computational resources and design time. Current inverse design approaches predominantly employ generative adversarial networks (GANs) and convolutional neural networks (CNNs). However, these models often produce blurry topological boundaries, rendering them inadequate for UWB filters that demand extremely precise modeling of intricate features, such as microscopic stubs and exceedingly narrow gaps. To overcome this bottleneck, this paper proposes an inverse design framework integrating a conditional diffusion model with an evolutionary algorithm. The conditional diffusion model is uniquely suited for UWB inverse design, directly incorporating target scattering parameters as explicit conditions during training strictly guides the generation trajectory. This mechanism enables the model to synthesize high-fidelity, fine-grained pixelated patterns that traditional networks fail to achieve. During the inverse design process, the conditional diffusion model generates candidate topology-guided target S-parameters. Subsequently, an evolutionary algorithm, coupled with a pre-trained ResNet fast evaluator, iteratively searches the latent space to pinpoint the optimal geometric structure matching the target EM response. Validation through multiple UWB filter examples demonstrates that simulated S-parameters exhibit excellent agreement with target curves, achieving an efficient and precise design from EM performance to geometric structure. The two final fabricated inverse-designed filters exhibit 3 dB passbands ranging from 5.67 to 12.40 GHz and 7.35 to 14.60 GHz, achieving high fractional bandwidths of 74.53% and 66.06%, respectively.