Rapid Yield Estimation of Microwave Passive Components Using Model-Order Reduction Based Neuro-Transfer Function Models

• Published in: IEEE microwave and wireless components letters Vol. 31; no. 4; pp. 333 - 336
• Main Authors: Zhang, Jianan, Feng, Feng, Zhang, Qi-Jun
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2021
• Subjects: Artificial neural networks; Computational modeling; Data models; Microwave theory and techniques; Model-order reduction (MOR); neural networks; Poles and zeros; statistical modeling; Training; transfer function; Yield estimation
• ISSN: 1531-1309; 1558-1764
• DOI: 10.1109/LMWC.2021.3059993

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.