基于改进斑马算法的GaN HEMT混合小信号建模

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关键词：GaN HEMT；小信号模型；斑马优化算法；参数提取方法；改进算法

中图分类号：TN386

文献标识码：A

DOI： 10.14106/j.cnki.1001-2028.2025.1472

引用格式：李畅， 王军.基于改进斑马算法的GaN HEMT混合小信号建模[J].电子元件与材料， 2025， 44（1）： 49-56.

Reference format： LI Chang， WANG Jun. GaN HEMT hybrid small-signal modeling based on improved Zebra algorithm [J]. Electronic Components and Materials， 2025， 44（1）： 49-56.

GaN HEMT hybrid small-signal modeling based on improved

Zebra algorithm

LI Chang， WANG Jun

（College of Information Engineering， Southwest University of Science and Technology， Mianyang 621010， Sichuan

Province，China）

Abstract：  To enhance small-signal modeling precision of the semiconductor device and avoid the local optimum of the optimization algorithm， a hybrid small-signal modeling method for Gallium Nitride High Electron Mobility Transistor （GaN HEMT） with Improved Zebra Optimization Algorithm （IZOA） was proposed. Small-signal parameters were extracted by the mathematical correction method and the direct extraction method to establish an initial model. The improved zebra optimization algorithm was apply to further boost modeling accuracy. The Zebra Optimization Algorithm （ZOA） improvements focused on three aspects： adopt chaotic mapping for initial population diversity; apply opposition-based learning strategy to enlarge search range; employ dynamic probability values rather than fixed to balance search and convergence. The experimental results show that， the average error of direct extraction method can be decreased from 3.47% to 0.19% by IZOA. Compared with the Grey Wolf Optimizer （GWO） algorithm （average error 0.95%）， it is reduced by 0.76%， and it is 0.33% lower than that of the ZOA （average error 0.52%）. Thus， the effectiveness and accuracy of the algorithm were verified.

Keywords：GaN HEMT; small signal model; Zebra optimization algorithm; parameter extraction method; improved algorithm

近年来，GaN HEMT器件因具备高禁带宽度、高电子迁移率和优秀的热稳定性而备受关注[1-2]。（剩余9431字）