Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements
Authors: Han Zhou, Richard Bannister, Caspar Pierce, Haojie Chang, David Widen, Ludvig Fornstedt, Gabriel Melin, Alexander Bohlin, +4 more
Organizations: Chalmers University of Technology, Gothenburg, Sweden · Advanced Technology Institute, University of Surrey, Guildford, UK · National Physical Laboratory, Teddington, UK.
Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time. This study uses a deep learning approach combining convolutional neural networks with genetic algorithms to automate pixelated microwave filter synthesis. To validate the approach experimentally, both S-parameter and spatial electric-field measurements were analyzed. The synthesized low-pass filter demonstrated excellent agreement between simulated and measured performance, achieving a 7 GHz passband with over 20 dB suppression beyond 9.5 GHz. Electro-optical measurements, for the first time, revealed electric field patterns that resemble coupled transmission-lines or stub structures, providing insight into the emergent characteristics of AI-generated designs.