基于通道电阻RL的改进GaN HEMT模型的热噪声分析
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西南科技大学 信息工程学院

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TN385

基金项目:

国家自然科学基金资助项目(69901003);四川省教育厅自然科学基金(18ZA0502)。


Thermal noise analysis of an improved GaN HEMT model based on channel resistance RL
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School of Information Engineering,Southwest University of Science and Technology,Mianyang

Fund Project:

Supported by the National Natural Science Foundation of China (69901003); Natural Science Foundation of Sichuan Provincial Education Department (18ZA0502).

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    摘要:

    本文提出了一种改进氮化镓(GaN)高电子迁移率晶体管(HEMT)等效电路模型的方法,利用通道电阻RL来提高模型的准确性。采用数学修正法和直接提取法提取小信号等效电路各元件参数并建立小信号模型,基于该模型,在常温下采用了典型的Pucel噪声模型和Pospieszalski模型对热噪声进行建模和仿真验证。通过对比改进前后模型参数的变化,验证了改进模型的有效性和精确性,可用于预测GaN HEMT器件的噪声性能。结果表明,改进后的模型比传统模型更精确,且栅偏置越高或频率越高,RL对模型的影响越大;相较于传统模型,改进模型的主要噪声参数的平均值更低,最小噪声系数NFmin减小了1.7%,等效噪声电阻Rn减小了2.7%,表现出较好的噪声特性。

    Abstract:

    This article proposed a method to improve the equivalent circuit model of gallium nitride high electron mobility transistors, utilizing channel resistance RL to improve the accuracy of the model.The mathematical correction method and direct extraction method are used to extract the parameters of various components in the small signal equivalent circuit and establish a small signal model. Based on this model, typical Pucel noise models and Pospieszalski models are used to model and simulate thermal noise at room temperature. By comparing the changes in model parameters before and after the improvement, the effectiveness and accuracy of the improved model were verified, which can be used to predict the noise performance of GaN HEMT devices. The results show that the improved model is more accurate than the traditional model, and the higher the gate bias or frequency, the greater the impact of RL on the model; Compared to traditional models, the improved model has a lower average value of the main noise figures, with a 1.7% reduction in the minimum noise coefficient NFmin and a 2.7% reduction in the equivalent noise resistance Rn, demonstrating good noise characteristics.

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  • 收稿日期:2023-07-20
  • 最后修改日期:2023-10-29
  • 录用日期:2023-10-30
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