Role of Machine Learning in Rapid Modeling of RF Devices: VO2 RF Switch Modeling as a Case Study

Ahmad Khusro, Shuai Yang, Mohammad Vaseem, Mohammad S. Hashmi, Atif Shamim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The paper provides an extensive overview on the potential of machine learning in rapid modeling of RF/Microwave devices. To demonstrate the role of machine learning in rapid modeling of RF devices, printed VO2 RF switch has been selected as an example. Of many supervised machine learning techniques, Artificial Neural Network (ANN) has been preferred to model the printed VO2 RF switch for reconfigurable applications. The model uses cascade feed-forward architecture of ANN along with Bayesian regularization algorithm to train the multi-input behavioral model with varied set of operating conditions (geometric dimensions and temperature) over a frequency range of 0.01 GHz to 35 GHz. Subsequently, the trained model is tested for novel set of test inputs for both interpolation and extrapolation cases to evaluate the accuracy, scalability, extrapolation capability and generalization ability. A very good performance metrics is obtained with the mean square error 3\mathrm{x}10^{-4} and 4.5\mathrm{x}10^{-4} for the interpolation and extrapolation set and correlation coefficient of 99% is obtained for the broad frequency range between the measured S-parameters and modeled which validates the effectiveness of the proposed modeling approach. Eventually, the potential of machine learning based model development is further enhanced by demonstrating the seamless integration of the model in CAD environment for real time design, simulation and analysis of bandwidth reconfigurable filters involving multiple ANN based VO2 RF switch equivalent model.
Original languageEnglish (US)
Title of host publication2021 IEEE Conference on Antenna Measurements & Applications (CAMA)
Number of pages6
ISBN (Print)9781728196978
StatePublished - Nov 15 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-04-05


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