Scholarlink

<p>As core equipment in semiconductor processing, thin-film fabrication and related fields, the impedance matching performance of radio-frequency power supplies directly determines power transmission efficiency and system stability. Traditional matching methods often encounter challenges such as slow convergence and poor load adaptability when dealing with dynamic loads and highly non-linear systems. Recent years have witnessed rapid advancements in artificial intelligence (AI) technologies, offering novel solutions for impedance matching. This paper systematically reviews the application progress of algorithms such as swarm intelligence optimization, machine learning modeling, and deep reinforcement learning control in Radio frequency (RF) power supply impedance matching. Finally, it discusses current challenges faced by AI in impedance matching and outlines future research directions to advance RF power supply impedance matching technology towards greater intelligence, adaptability, and system synergy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of Artificial Intelligence in Solid-State Impedance Matching for Radio Frequency Power Supplies: A Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月10日

查看全文
  • Fei Ge,
  • Guidong Zhang,
  • Weiqun Lin,
  • Tao Liu,
  • Wenjie Zhang,
  • Weiping Le,
  • Yun Zhang

摘要

As core equipment in semiconductor processing, thin-film fabrication and related fields, the impedance matching performance of radio-frequency power supplies directly determines power transmission efficiency and system stability. Traditional matching methods often encounter challenges such as slow convergence and poor load adaptability when dealing with dynamic loads and highly non-linear systems. Recent years have witnessed rapid advancements in artificial intelligence (AI) technologies, offering novel solutions for impedance matching. This paper systematically reviews the application progress of algorithms such as swarm intelligence optimization, machine learning modeling, and deep reinforcement learning control in Radio frequency (RF) power supply impedance matching. Finally, it discusses current challenges faced by AI in impedance matching and outlines future research directions to advance RF power supply impedance matching technology towards greater intelligence, adaptability, and system synergy.