The purpose of this paper is to provide a comprehensive overview of the field‐effect transistor (FET) small‐signal modeling using artificial neural networks (ANNs). To gain an in‐depth insight into how to effectively develop an ANN model, we present a comparative study on the application of the ANNs for modeling the scattering (S‐) parameters of a variety of FET technologies versus bias point, ambient temperature, and geometrical dimensions. As will be shown, the main challenge consists of identifying the most appropriate ANN model for the specific case under study. This is because the performance of an ANN‐based model can vary significantly, depending especially on the choice of the model structure and the size and parameters of the chosen ANN. In addition, the choice of the model is related directly to the behavior of the FET characteristics, which might greatly depend on the selected device technology and operating conditions. The analysis of the present comparative study allows understanding how to properly construct ANN models to perform at their best for a successful FET modeling.

中文翻译：

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人工神经网络在微波FET小信号建模中的应用综述

本文的目的是使用人工神经网络（ANN）全面概述场效应晶体管（FET）小信号建模。为了深入了解如何有效开发ANN模型，我们对ANN在散射建模（S-）各种FET技术的参数与偏置点，环境温度和几何尺寸的关系。如将显示的那样，主要挑战在于为正在研究的特定案例确定最合适的ANN模型。这是因为基于ANN的模型的性能可能会发生显着变化，尤其取决于模型结构的选择以及所选ANN的大小和参数。此外，模型的选择与FET特性的行为直接相关，这可能在很大程度上取决于所选的器件技术和工作条件。通过本比较研究的分析，可以了解如何正确构建ANN模型，以使其在成功进行FET建模时发挥最佳作用。