Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics

This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (۴.۵۴ GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better quality factor. Several techniques are used to enhance sensitivity, including a Photonic Band Gap (PBG), corner cut, and slow-wave vias. These techniques increase the interaction between the material under test and the electric field. By utilizing slow-wave vias, ۳۵% size reduction is achieved. Achieving simultaneous miniaturization and sensitivity enhancement in this study introduces a new possibility and application for sensor design. The values of complex permittivities are extracted from scattering parameters obtained from simulation of the structure in CST Microwave Studio (MWS) using a machine learning approaches. Our sensor has ۰.۸% sensitivity, which is better than that of other sensors. Moreover, the maximum error rate in our method is lower than other existing methods. This ratio for the proposed method is ۲.۳۱% while for curve fitting and analytical solutions are ۲۶% and ۱۶%, respectively.

Keywords:

Complex Permittivity , Machine Learning (ML) , Photonic Band Gap , Slow-Wave , Substrate Integrated Waveguide (SIW).