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<p>This paper presents a thermal analysis of the 0.34-THz Extended Interaction Klystron (EIK) high-frequency structure and proposes a time-resolved forward-prediction network (TR-FPN-RNN) as a fast surrogate for time-domain particle-in-cell (PIC) simulations. The heat source distribution and trajectory-related beam transport characteristics, quantified by beam transmission efficiency and interception ratio, can be rapidly predicted by the proposed method. The heat sources of the 0.34 THz EIK high-frequency structure are analyzed with the output power of 92.8 W, and a gain of 36.67&#xa0;dB. The coincident results verify the practicability of the TR-FPN-RNN method, and the averaged errors are below 2%. The TR-FPN-RNN method and the thermal analysis technologies indicate significant potentials for vacuum electron devices in the millimeter-wave and terahertz spectrum.</p>

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Thermal Analysis with Recurrent Neural Network for 0.34 THz Extended Interaction Klystron

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Zeyuan Yang,
  • Guo Guo,
  • Hongchao Wang,
  • Haofei Li,
  • Taifu Zhou,
  • Zhigang Wang,
  • Bo Yan,
  • Yanyu Wei

摘要

This paper presents a thermal analysis of the 0.34-THz Extended Interaction Klystron (EIK) high-frequency structure and proposes a time-resolved forward-prediction network (TR-FPN-RNN) as a fast surrogate for time-domain particle-in-cell (PIC) simulations. The heat source distribution and trajectory-related beam transport characteristics, quantified by beam transmission efficiency and interception ratio, can be rapidly predicted by the proposed method. The heat sources of the 0.34 THz EIK high-frequency structure are analyzed with the output power of 92.8 W, and a gain of 36.67 dB. The coincident results verify the practicability of the TR-FPN-RNN method, and the averaged errors are below 2%. The TR-FPN-RNN method and the thermal analysis technologies indicate significant potentials for vacuum electron devices in the millimeter-wave and terahertz spectrum.