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A deep neural network based tuning technique of lossy microwave coupled resonator filters
Author(s) -
Sun JiaJing,
Sun Sheng,
Yu Xi,
Chen Yongpin P.,
Hu Jun
Publication year - 2019
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.31866
Subject(s) - microwave , lossy compression , return loss , electronic engineering , resonator , band pass filter , artificial neural network , filter (signal processing) , insertion loss , coupling (piping) , convolution (computer science) , waveguide filter , computer science , prototype filter , materials science , engineering , filter design , telecommunications , optoelectronics , artificial intelligence , antenna (radio) , metallurgy , computer vision
A novel tuning process of microwave filter is proposed based on deep neural network. Convolutional neural networks (CNNs) with several layers of convolution and nonlinear units are introduced to extract the coupling matrix of the simulated responses during the design procedure of lossy microwave filters. Then the proposed neural network model has been used to guide the tuning process of substrate integrated waveguide (SIW) filters. Compared to the traditional tuning method, this method can extract the coupling matrix with high accuracy as well as providing directions for the tuning rapidly. As an example, a fourth‐order bandpass filter centered at 20.45 GHz with 1.72‐dB insertion loss and 21.49‐dB return loss is finally designed using the proposed model. Numerical example validates the effectiveness of the proposed method.