In this work, physical parameter‐based modeling of small signal parameters for a metal‐semiconductor field‐effect transistor (MESFET) has been carried out as continuous functions of drain voltage, gate voltage, frequency, and gate width. For this purpose, a device simulator has been used to generate a big dataset of which the physical device parameters included material type, doping concentration and profile, contact type, gate length, gate width, and work function. Five state‐of‐the‐art algorithms: multi‐layer perceptron (MLP), IBk, K^*, Bagging, and REPTree have been used for creating a regression model. The symbolic regression algorithm has been used to obtain analytical expressions of the real and imaginary parts of the Scattering (S) parameters using the physics‐based generated dataset. The regression performances of all the benchmarks and the symbolic regression have been compared to references from the device simulator results. The results of the derived equations and the best algorithms have been then compared to the device simulator results, with case studies for validation. The DC performance characteristics of the MESFET have been also obtained. The proposed model can be used to predict the small signal parameters of new devices prior to development, and allows for both the device and circuit to be optimized for specific applications.

中文翻译：

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金属半导体场效应晶体管小信号参数的基于物理参数的数据驱动建模

在这项工作中，作为漏极电压，栅极电压，频率和栅极宽度的连续函数，对金属半导体场效应晶体管（MESFET）的小信号参数进行了基于物理参数的建模。为此，已使用器件模拟器来生成大型数据集，其物理器件参数包括材料类型，掺杂浓度和分布，接触类型，栅极长度，栅极宽度和功函数。五个最先进的算法：多层感知器（MLP），IBk，K ^*，装袋和REPTree已用于创建回归模型。使用基于物理的生成数据集，已使用符号回归算法来获取散射（S）参数的实部和虚部的解析表达式。已将所有基准测试的回归性能和符号回归与来自设备模拟器结果的参考进行了比较。然后将导出的方程式和最佳算法的结果与设备模拟器的结果进行比较，并进行案例研究以进行验证。还获得了MESFET的直流性能特征。提出的模型可用于在开发之前预测新设备的小信号参数，并允许针对特定应用优化设备和电路。