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<p>As Artificial Intelligence (AI) technologies rapidly advance, the application bands of microwave filters have expanded, and their deployment scenarios have become increasingly diversified. Consequently, the exponential complexity of design parameters has rendered traditional design methodologies inadequate for high-efficiency iteration and stringent electromagnetic compatibility (EMC) requirements. Thus, AI-driven Computer-Aided Design (CAD) techniques have emerged as a critical research frontier. This paper presents a comprehensive state-of-the-art review of Machine Learning (ML) and Deep Learning (DL) applications in microwave filter design. We first systematically categorize microwave filters and elaborate on their critical performance metrics across both frequency and time domains. Subsequently, we trace the evolutionary trajectories of ML and DL algorithms, establishing a foundational framework to map the development of intelligent CAD techniques in this domain. The core of this review critically categorizes and analyzes existing research based on distinct neural network architectures, alongside hybrid approaches that integrate classical optimization algorithms with AI. Finally, in the context of the emerging era of large-scale AI models, we summarize current paradigms in filter optimization, fault diagnosis, and innovative design, while outlining future perspectives and challenges to drive transformative breakthroughs in this interdisciplinary field.</p>

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Machine Learning and Deep Learning in Microwave Filter Design: A Comprehensive State-of-the-Art Review

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Yaogen Li,
  • Chengjie Li,
  • Yinxiang Jin,
  • Haichun Wang,
  • Mingjie Sheng,
  • Jinjing Ren

摘要

As Artificial Intelligence (AI) technologies rapidly advance, the application bands of microwave filters have expanded, and their deployment scenarios have become increasingly diversified. Consequently, the exponential complexity of design parameters has rendered traditional design methodologies inadequate for high-efficiency iteration and stringent electromagnetic compatibility (EMC) requirements. Thus, AI-driven Computer-Aided Design (CAD) techniques have emerged as a critical research frontier. This paper presents a comprehensive state-of-the-art review of Machine Learning (ML) and Deep Learning (DL) applications in microwave filter design. We first systematically categorize microwave filters and elaborate on their critical performance metrics across both frequency and time domains. Subsequently, we trace the evolutionary trajectories of ML and DL algorithms, establishing a foundational framework to map the development of intelligent CAD techniques in this domain. The core of this review critically categorizes and analyzes existing research based on distinct neural network architectures, alongside hybrid approaches that integrate classical optimization algorithms with AI. Finally, in the context of the emerging era of large-scale AI models, we summarize current paradigms in filter optimization, fault diagnosis, and innovative design, while outlining future perspectives and challenges to drive transformative breakthroughs in this interdisciplinary field.