Neural network of calibrated coarse model and application to substrate integrated waveguide filter design

• Published in: International journal of RF and microwave computer-aided engineering Vol. 30; no. 10
• Main Authors: Du, Gong‐Yuan, Jin, Long
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2020
• Subjects: calibrated coarse model; Calibration; equivalent de‐embedding technique; Filter design (mathematics); inverse neural network; Neural networks; SIW filter; Substrate integrated waveguides; Synthesis; synthesize; Training; Waveguide filters
• ISSN: 1096-4290; 1099-047X
• DOI: 10.1002/mmce.22374

In this article, we propose a novel neural network of calibrated coarse model, which can obtain the optimal filter response with as little training data as possible to synthesize the entire substrate integrated waveguide (SIW) filter. By incorporating the knowledge of filter decomposition with the inverse neural network, we build a coarse model that can synthesize the dimensions of a SIW filter. However, the SIW structures are subject to a potential leakage problem due to the periodic gaps, the results of the coarse model are very different from the ideal response. We propose a novel calibrated neural network from the perspective of the coupling matrix to correct the errors generated in the coarse model. In addition, this article also proposes an equivalent de‐embedding technique, which is simpler than the thru‐reflect‐line calibration technique to accurately extract the scattering parameters of the SIW discontinuities. An H‐plane fifth order SIW filter is synthesized by the proposed model. The result shows that the SIW filter that is very close to the ideal response can be synthesized with only a few hundred training data.