Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements

Abstract: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.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Systems and Control (eess.SY)

arXiv:2606.18402 [eess.SP]

(or arXiv:2606.18402v2 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2606.18402 Focus to learn more arXiv-issued DOI via DataCite

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