Optimization of ANN-based models and its EM co-simulation for printed RF devices

Yang S. Khusro A. Li W. Vaseem M. Hashmi M. Shamim A.
March 2022 John Wiley and Sons Inc

International Journal of RF and Microwave Computer-Aided Engineering
2022 #32 Issue 3

Printed VO₂ RF switch founds immense potential in RF reconfigurable applications. However, their generic electrical equivalent model is still intangible that can be further integrated in CAD tools and utilize for simulation, analysis and design of RF/microwave circuits and systems. The artificial neural network (ANN) has been gaining popularity in modeling various types of RF components. However, most of these works merely demonstrate the establishment of the ANN-based RF model in the MATLAB environment without involving significant optimization. Furthermore, the integration of such ANN-based RF models in the EM and circuit simulator as well as the co-simulation between the ANN-based model and conventional models have not been demonstrated or validated. Therefore, the earlier reported models are still one step removed from its real RF applications. In this work, by using the fully printed vanadium dioxide (VO₂) RF switch as the modeling example, a systematic hyperparameter optimization process has been conducted. Compared to the non-optimized ANN model, a dramatic improvement in the models accuracy has been observed for the ANN model with fully optimized hyperparameters. A correlation coefficient of more than 99.2% for broad frequency range demonstrates the accuracy of the modeling technique. In addition, we have also integrated the Python-backed ANN-based model into Advanced Design System (ADS), where a reconfigurable T-resonator band stop filter is used as an example to demonstrate the co-simulation between the ANN-based model and the conventional lumped-based model.

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CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India
School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Nur-Sultan, Kazakhstan

CEMSE Division
Electrical Engineering Department
School of Engineering and Digital Sciences (SEDS)

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026