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

Shuai Yang, Ahmad Khusro, Weiwei Li, Mohammad Vaseem, Mohammad Hashmi, Atif Shamim

2021

Closed access Metadata (CSL-JSON)
DOI 10.23919/eumc48046.2021.9338125
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Fully printed vanadium dioxide (VO2) based Radio Frequency (RF) switches have been recently developed for advanced frequency-reconfigurable RF electronics. A reliable and versatile model for the VO2switches is required for design and simulations in the modern Computer-Aided Design (CAD) tools. This paper proposes a machine learning (ML) based model for VO2RF 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 × 10-4and 5 × 10-3for the magnitude and phase values over the complete frequency range.