In this letter, we propose a novel technique for rapid and accurate yield estimation of microwave passive components using model-order reduction (MOR)-based neuro-transfer function (neuro-TF) models. In the proposed technique, the frequency responses of microwave components are represented by transfer functions in the pole-zero-gain format. The poles, zeros, and gain in the transfer functions are computed by the MOR technique. Neural networks are trained to capture the dynamic changes of the poles/zeros/gain as the statistical/geometrical variables change. A refinement training process is designed to further align the outputs of the neuro-TF model. Once developed, the MOR-based neuro-TF model can provide rapid and accurate prediction of electromagnetic (EM) behavior of microwave passive components, thereby accelerating EM-based yield estimation. To achieve similar yield estimation accuracy, the proposed technique requires a shorter CPU time than existing yield estimation methods. The advantages of the proposed technique are illustrated by two microwave examples.

中文翻译：

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基于模型阶约的神经传递函数模型的微波无源元件的快速产率估算

在这封信中，我们提出了一种新的技术，该技术可使用基于模型阶数减少（MOR）的神经传递函数（neuro-TF）模型快速准确地估计微波无源元件的良率。在提出的技术中，微波分量的频率响应由零极增益格式的传递函数表示。传递函数中的极点，零点和增益是通过MOR技术计算的。神经网络经过训练可以捕获统计/几何变量变化时极点/零点/增益的动态变化。设计完善的训练过程以进一步对齐神经TF模型的输出。一旦建立起来，基于MOR的神经TF模型就可以快速准确地预测微波无源组件的电磁（EM）行为，从而加快基于EM的产量估算。为了获得相似的产量估算精度，与现有的产量估算方法相比，所提出的技术需要更短的CPU时间。通过两个微波示例说明了所提出技术的优势。