毕磊. 基于非线性电阻的GaN HEMT经验基大信号模型[J]. 电波科学学报,2021,36(5):730-736. DOI: 10.13443/j.cjors.2020072801
      引用本文: 毕磊. 基于非线性电阻的GaN HEMT经验基大信号模型[J]. 电波科学学报,2021,36(5):730-736. DOI: 10.13443/j.cjors.2020072801
      BI L. Empirical basis large signal model of GaN HEMT based on nonlinear resistance[J]. Chinese journal of radio science,2021,36(5):730-736. (in Chinese). DOI: 10.13443/j.cjors.2020072801
      Citation: BI L. Empirical basis large signal model of GaN HEMT based on nonlinear resistance[J]. Chinese journal of radio science,2021,36(5):730-736. (in Chinese). DOI: 10.13443/j.cjors.2020072801

      基于非线性电阻的GaN HEMT经验基大信号模型

      Empirical basis large signal model of GaN HEMT based on nonlinear resistance

      • 摘要: 为了实现建立准确的氮化镓高电子迁移率晶体管(GaN high electron mobility transistor,GaN HEMT)大信号模型的目的,提出了一种基于非线性电阻的GaN HEMT经验基大信号模型. 通过对GaN HEMT在高漏极偏置和高电流密度下的电阻特性分析,将受漏源电流控制的非线性电阻模型嵌入经验基大信号模型中. 结合Matlab和ADS提取模型初值,在ADS中建立完整的大信号符号定义模型. 选择栅长为0.25 μm,栅宽分别为2×200 μm、2×250 μm、4×200 μm的GaN HEMT进行直流输出特性和大信号输出特性仿真验证,结果表明本文模型与测试数据具有较高的吻合性. 该大信号模型提高了经验基大信号模型的物理特性,具有较高的精度及良好的缩放性.

         

        Abstract: In order to achieve the purpose of accurately establishing the large-signal model of GaN high electron mobility transistors (HEMTs), an empirical-based large-signal model of GaN high-electron mobility transistors based on nonlinear resistance is proposed. The resistance characteristics under high drain bias and high current density is analyzed, and the nonlinear resistance model controlled by the drain-source current is embedded into the empirical large-signal model. Combing Matlab and ADS to extract the initial value of the model, a complete large-signal symbol definition model is established in ADS . Select GaN HEMTs with a gate length of 0.25 μm and a gate width of 2×200 μm, 2×250 μm, 4×200 μm for DC output characteristics and large signal output characteristics simulation verification. The simulation result proves that the model is in good agreement with the test data. This large-signal model improves the physical characteristics of the experience-based large-signal model, and has high accuracy and good scalability.

         

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