Analysis of a microwave filter parameters for design optimization via machine learning

Microwave filters are essential for filtering and selecting frequencies. They prevent interference, enhance signal quality, and maximize the efficiency of microwave systems. They are widely employed in wireless communications, satellites, and radar systems. To optimize the design of these filters, techniques like machine learning can be utilized, saving time and resources compared to simulations. Machine learning learns from data to discover optimized solutions more swiftly and efficiently, leading to high-performance filters. In this work, analyses of the parameters of a T-inverted filter were performed to serve as a foundation for a machine learning-based design. The filter used was designed and manufactured for a frequency range of 1 GHz to 3 GHz and its measured and simulated results were compared as a way of validating the structure.