加权算法在增强型宽带矢量网络分析仪测量中的应用

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Sang-hee Shin, James Skinner
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引用次数: 0

摘要

本文介绍了一种应用于直通反射线路(TRL)校准技术的加权算法,以提高宽频带矢量网络分析仪(VNA)测量的准确性和可靠性。该方法解决了传统 TRL 校准的固有局限性,特别是在使用多个线路标准时,在带状 TRL 方法中观察到的阶跃变化。通过引入一个定制的加权函数,为每个线路标准分配与相位相关的权重,可以实现更平滑的转换和更好的 S 参数测量。通过对 3.5 毫米和 N 型器件的测量进行实验验证,证明了加权-TRL 方法在消除各种频率范围内的不连续性和校准伪影方面的有效性。结果表明,与传统的 TRL 校准方法相比,这种方法可以改进 S 参数的校准。所开发的加权-TRL 校准技术在计量级测量方面取得了重大进展,可在宽频带范围内对高频器件进行更精确的表征。通过缓解 TRL 校准的一个关键限制,该方法为提高精密计量应用中 VNA 测量的准确性和可靠性提供了宝贵的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Weighting Algorithm for Enhanced Broadband Vector Network Analyzer Measurements
A weighting algorithm for application in the Thru-Reflect-Line (TRL) calibration technique is presented to enhance the accuracy and reliability of vector network analyzer (VNA) measurements over broad frequency bands. The method addresses the inherent limitations of the traditional TRL calibration, particularly the step changes observed in banded-TRL approaches when multiple Line standards are used. By introducing a bespoke weighting function that assigns phase-dependent weights to each Line standard, smoother transitions and improved S-parameter measurements can be achieved. Experimental validation using measurements of both 3.5 mm and Type-N devices demonstrates the effectiveness of the weighted-TRL method in eliminating discontinuities and calibration artifacts across a wide range of frequencies. The results reveal the improved calibration of S-parameters this approach can yield compared to traditional TRL calibration methods. The developed weighted-TRL calibration technique offers a significant advancement in metrology-grade measurements, enabling more precise characterization of high-frequency devices across broad frequency bands. By mitigating a key limitation of the TRL calibration, this method provides a valuable tool for enhancing the accuracy and reliability of VNA measurements for precision metrology applications.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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