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<p>The small-signal intrinsic noise behavior of a&#xa0;GaN high electron mobility transistors (HEMTs) was modeled using the whale optimization algorithm-hybrid kernel extreme learning machine (WOA-HKELM) algorithm. This algorithm not only addresses the issues associated with extreme learning machine (ELM), such as the challenge of determining the number of hidden-layer nodes and the occurrence of overfitting, but also identifies the optimal regularization coefficient C and kernel parameters S that support the HKELM algorithm through the utilization of the WOA. To verify the superiority of the proposed WOA-HKELM algorithm, small-signal noise modeling experiments were conducted on GaN HEMT devices with different gate dimensions under various bias conditions. The modeling performance of WOA-HKELM was compared with that of the improved Sparrow Search Optimized Hybrid Kernel Extreme Learning Machine (MShOA-HKELM), HKELM, and other algorithms. Experimental demonstrate that the&#xa0;WOA-HKELM achieves higher accuracy and stability in modeling the intrinsic small-signal noise characteristics of GaN HEMTs.</p>

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Intrinsic noise behavioral modeling of GaN HEMTs under small-signal conditions using WOA-HKELM

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Kexin Wang,
  • Jinchan Wang,
  • Shaojie Zheng,
  • Min Liu,
  • Jincan Zhang

摘要

The small-signal intrinsic noise behavior of a GaN high electron mobility transistors (HEMTs) was modeled using the whale optimization algorithm-hybrid kernel extreme learning machine (WOA-HKELM) algorithm. This algorithm not only addresses the issues associated with extreme learning machine (ELM), such as the challenge of determining the number of hidden-layer nodes and the occurrence of overfitting, but also identifies the optimal regularization coefficient C and kernel parameters S that support the HKELM algorithm through the utilization of the WOA. To verify the superiority of the proposed WOA-HKELM algorithm, small-signal noise modeling experiments were conducted on GaN HEMT devices with different gate dimensions under various bias conditions. The modeling performance of WOA-HKELM was compared with that of the improved Sparrow Search Optimized Hybrid Kernel Extreme Learning Machine (MShOA-HKELM), HKELM, and other algorithms. Experimental demonstrate that the WOA-HKELM achieves higher accuracy and stability in modeling the intrinsic small-signal noise characteristics of GaN HEMTs.