Аннотация:
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 nonoptimized
ANN model, a dramatic improvement in the model's 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.