Electronics, Vol. 14, Pages 1989: A Generative Model-Based Method for Inverse Design of Microstrip Filters

Electronics doi: 10.3390/electronics14101989

Authors:
Haipeng Wang
Chenchen Nie
Zhongfang Ren
Yunbo Li

In the area of microstrip filter design and optimization, deep learning (DL) algorithms have become much more attractive and powerful in recent years. Here, we propose a method to realize the inverse design of passive microstrip filters, applying generative adversarial networks (GANs). The proposed DL-assisted framework is composed of three components, including a compositional pattern-producing network GAN-based graphic generator, a convolution neural network (CNN)-based electromagnetic (EM) response predictor, and a genetic algorithm optimizer. The filter adopts a square patch resonator structure with an irregular-graphic slot and corner-cuts introduced at diagonal positions. By constructing a hybrid model of pixelated patterns in the filter structures and the corresponding EM response S-parameters, we can obtain customized filter solutions with wideband and dual-band magnitude responses in the 3–8 GHz and 1–6 GHz frequency range, respectively. For each inverse design, it cost 3.6 min for executing 1000 iterations, on average. Numerical simulations and experimental results show that the S-parameters of the generated filters are in excellent agreement with the self-defined targets.

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Haipeng Wang www.mdpi.com