This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

中文翻译：

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基于时延神经网络的非线性微波设备建模技术。

本文提出了一种基于时延神经网络（TDNN）的非线性微波设备建模技术。与静态神经网络建模方法相比，该技术可以准确地对非线性微波设备进行建模。开发了一种新的公式，以允许使用DC，小信号和大信号数据对建议的TDNN模型进行训练，从而可以增强设备模型的通用性。提出了一种算法，可以有效地训练提出的TDNN模型。GaAs金属半导体场效应晶体管（MESFET）和GaAs高电子迁移率晶体管（HEMT）实例验证了此提议的技术。这两个例子表明，所提出的TDNN是对各种类型的非线性微波设备进行建模的有效方法。