Machine Learning-Driven Approaches for Advanced Microwave Filter Design

This study introduces a machine learning (ML)-driven approach to next-generation microwave filter design that enhances both accuracy and efficiency via repeated refinement. The approach includes generating a coupling matrix from filter specifications, followed by predicting physical parameters such as iris widths and resonator lengths using ML models, especially with the XGBoost algorithm. These predictions are validated and tuned via simulations and iterative adjustments to ensure meeting the performance criteria, such as center frequency, bandwidth, and return loss. For tuning, in this work, we used Simulated Annealing to extract a coupling matrix to reduce errors and hence allow accurate further optimization. The predicted values before optimization are more than 90 percent accurate compared to the optimized values, significantly reducing the optimization time and the number of iterations required. To demonstrate the procedure’s validity, third-, fourth-, and fifth-order filters are implemented, which shows significant improvements in design efficiency and accuracy.