Abstract:
Designing high-performance microwave and
millimeter-wave filters is difficult because
small changes in geometric dimensions and
electrical sizes can significantly affect the filter’s
characteristic. Typically, in filter design,
the initial values of design variables
are optimized to achieve the desired performance.
In the field of high-frequency RF device
modeling, the use of machine learning (ML)
through artificial neural networks (ANN) has
gained popularity in recent years. Unlike other
RF modeling techniques, ANN-based models
require training with sufficient datasets to
achieve the desired accuracy level. The input
data could be the device’s dimensions, while
the output could be the S-parameters. Once
trained, the ANN-based model can provide
EM-level accuracy and equivalent-circuit-level
speed. Additionally, it is highly scalable, allowing
for the introduction of more input parameters
to make the model more versatile and
complex. Therefore, the ANN-based model is
an excellent option for high-frequency RF modeling
compared to other techniques. The main
objective of this research project is to develop
an AAN that can be used in design of RF Filters.