A novel space mapping (SM) modeling approach for gallium nitride (GaN) high-electron-mobility transistors (HEMTs) with trapping effects is presented in this article, advancing the SM technique for nonlinear device modeling. Existing SM modeling approach uses an external mapping to map an existing device model onto device data. When different branches inside the existing device model need to address very different behaviors, such as trapping effects and frequency dispersion in GaN HEMTs, it is hard for one external mapping to simultaneously map different behaviors. The proposed SM technique develops separate mappings for different branches, such that different behaviors can be mapped separately. Each mapping module is formulated to map a specific behavior in the overall model. Each mapping module is developed through machine learning to systematically overcome the gap between each internal branch and each set of target data, accelerating the process of model development. The proposed SM technique is a fast and systematic modeling approach, compared with the existing empirical function/equivalent circuit approach. Compared with the pure neural network modeling approach, the proposed SM technique employs less training data. Measurement data of a $2\times 350\,\,\mu \text{m}$ GaN HEMT device are employed for model training and verification. Good agreement can be achieved between the developed large-signal model and the measurement data, including dc, pulsed I–V (PIV) at seven quiescent biases, $S$ -parameters, and power characteristics. Reasonably close predictions of load–pull figures of merit are achieved by the developed model. The model development illustrated in the example shows that the proposed SM technique is a fast modeling approach to develop an accurate large-signal model for GaN HEMTs.

中文翻译：

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使用分解映射进行 GaN HEMT 建模的空间映射技术

本文介绍了一种用于具有俘获效应的氮化镓 (GaN) 高电子迁移率晶体管 (HEMT) 的新型空间映射 (SM) 建模方法，推进了用于非线性器件建模的 SM 技术。现有的 SM 建模方法使用外部映射将现有设备模型映射到设备数据。当现有器件模型中的不同分支需要处理非常不同的行为时，例如 GaN HEMT 中的俘获效应和频率色散，一个外部映射很难同时映射不同的行为。所提出的 SM 技术为不同的分支开发了单独的映射，以便可以单独映射不同的行为。每个映射模块都被制定来映射整个模型中的特定行为。每个映射模块都通过机器学习开发，系统地克服每个内部分支与每组目标数据之间的差距，加速模型开发过程。与现有的经验函数/等效电路方法相比，所提出的 SM 技术是一种快速且系统的建模方法。与纯神经网络建模方法相比，所提出的 SM 技术使用较少的训练数据。$2\times 350\,\,\mu \text{m}$ GaN HEMT 器件的测量数据用于模型训练和验证。开发的大信号模型与测量数据（包括直流、七个静态偏置下的脉冲 I-V (PIV)、$S$ 参数和功率特性）之间可以实现良好的一致性。所开发的模型实现了对负载-牵引品质因数的合理接近的预测。示例中说明的模型开发表明，所提出的 SM 技术是一种快速建模方法，可为 GaN HEMT 开发准确的大信号模型。