A Machine Learning-Based Microwave Device Model for Fully Printed VO2 RF Switches

Fully printed vanadium dioxide (VO
₂
) based Radio Frequency (RF) switches have been recently developed for advanced frequency-reconfigurable RF electronics. A reliable and versatile model for the VO
₂
switches is required for design and simulations in the modern Computer-Aided Design (CAD) tools. This paper proposes a machine learning (ML) based model for VO
₂
RF switches, which is much more time and resource efficient as compared to the traditional device models. The computational efficiency, accuracy and robustness of the proposed model over a frequency range of 30 GHz is demonstrated through an excellent agreement between the modelled and measured results. The comparison between the measured and modelled results demonstrate a mean-square error (MSE) of lower than 5 × 10
^-4
and 5 × 10
^-3
for the magnitude and phase values over the complete frequency range.