Parametric modeling of electromagnetic (EM) behaviors has become important for EM design optimizations of microwave components. This paper provides an overview of recent advances in parametric modeling of microwave components using combined neural network and transfer function (neuro‐TF). Transfer functions are used to represent the EM responses of passive components vs frequency. With the help of the transfer function, the nonlinearity of the neural network structure can be significantly decreased. We first introduce the neuro‐TF modeling approach in rational format. We also review the pole‐residue‐based neuro‐TF modeling technique. The orders of the pole‐residue transfer functions may vary over different regions of geometrical parameters. A pole‐residue tracking technique can be used to solve this order‐changing problem. As a further advancement, we discuss the sensitivity analysis‐based neuro‐TF modeling technique. The purpose is to increase the model accuracy by utilizing EM sensitivity information and to speed up the model development process by reducing the number of training data required for developing the model. After the modeling process, the trained model can be used to provide accurate and fast prediction of the EM responses w.r.t. the geometrical variables and can be subsequently used in the high‐level circuit and system design.

中文翻译：

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使用组合神经网络和传递函数的微波组件参数化建模的最新进展

电磁（EM）行为的参数化建模对于微波组件的EM设计优化已变得非常重要。本文概述了使用组合神经网络和传递函数（neuro-TF）进行的微波组件参数建模的最新进展。传递函数用于表示无源组件对频率的电磁响应。借助于传递函数，可以大大减少神经网络结构的非线性。我们首先介绍理性格式的Neuro-TF建模方法。我们还将回顾基于极残基的神经TF建模技术。极点残差传递函数的阶数可能会在几何参数的不同区域变化。极点残差跟踪技术可用于解决此顺序更改问题。作为进一步的进步，我们讨论了基于灵敏度分析的Neuro-TF建模技术。目的是通过利用EM灵敏度信息来提高模型准确性，并通过减少开发模型所需的训练数据的数量来加快模型开发过程。在建模过程之后，经过训练的模型可用于通过几何变量提供对EM响应的准确，快速的预测，并可随后用于高级电路和系统设计中。