用深度学习自动设计微波滤波器，实测性能与仿真高度一致。
Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements
- 结合卷积神经网络与遗传算法，实现像素化滤波器的自动化生成。
- 设计的低通滤波器通带达7 GHz，9.5 GHz以上抑制超20 dB。
- 首次通过电光测量揭示AI设计的电场分布特征，助力理解其物理机制。
传统微波滤波器设计依赖迭代参数调优和预设拓扑，限制了设计空间并延长开发周期。本研究采用深度学习方法，结合卷积神经网络与遗传算法，实现像素化微波滤波器的自动化合成。为实验验证，同时分析S参数与空间电场测量数据。合成的低通滤波器在仿真与实测间表现高度一致，具备7 GHz通带，且在9.5 GHz以上抑制超过20 dB。电光测量首次揭示了人工智能生成设计中的电场分布模式，呈现耦合传输线或枝节结构特征，为理解AI设计的涌现特性提供了物理洞察。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。