Space Mapping Technique on Microwave Filter Circuit Model Using Artificial Neural Network for Parameter Extraction

This research introduces an innovative approach to microwave filter design, utilizing an artificial neural network-based model that learns the relationship between geometric parameters and the microwave filter's response. In particular, the proposed model is applied to the design of a 3-pole Chebyshev capacitively lossless bandpass microwave filter. Two examples with different center frequencies and percentage bandwidths are considered to validate the model's effectiveness. Distinct datasets are generated for each center frequency and percentage bandwidth to ensure accuracy. The trained ANN models are used for parameter extraction at each aggressive space mapping iteration, offering a fast and accurate solution for microwave filter design. This approach provides a simple yet promising alternative to traditional design methods.