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Measurement-based modeling of vector network analyzer calibration standards and nonlinear microwave devices using artificial neural networks.

机译：矢量网络分析仪校准标准和基于人工神经网络的非线性微波设备的基于测量的建模。

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摘要

This thesis is comprised of two parts. The first segment covers artificial neural network (ANN) modeling for improved vector network analyzer (VNA) calibrations. Specifically, measurement-based ANNs are applied to model a variety of on-wafer and coaxial vector network analyzer calibrations, including open-short-load-thru (OSLT) and line-reflect-match (LRM). A sensitivity analysis of the ANNs is performed by determining the training error as functions of the number of hidden neurons and the number of training points. The respective accuracies of these calibrations are then assessed using the ANN-modeled standards. As a major research result, this doctoral thesis shows that ANN models offer a number of advantages over using calibrated measurement data files or equivalent circuit models, namely: they do not require the numerous details and parameters of physical models; calibration times can be reduced because only a few training points are required to accurately model the standards; ANN model descriptions are much more compact than large measurement data files; ANN models, trained on only a few measurement points can be much more accurate than direct calibrations when limited calibration data are available; ANNs give an optimized estimate in the presence of noise; and ANN models are able to accurately model loads with measured DC resistances slightly outside of their training range.; In the second part of this thesis, new frequency-domain models and figures of merit for nonlinear microwave circuits are developed for sparse-tone inputs. This section begins with a method for preserving time-invariant phase relationships when ratios are taken between two harmonically related signals by introducing a third signal that is used as a phase reference. Then, as another major research result, this doctoral thesis introduces nonlinear large-signal scattering ( S ) parameters, a new type of frequency-domain mapping that relates incident and reflected signals. A general form of nonlinear large-signal S -parameters is presented. It is shown that they reduce to classic S-parameters in the absence of nonlinearities. Nonlinear large-signal impedance ( Z ) and admittance ( A ) parameters are also introduced, and equations relating the different representations are derived. Next, definitions of power gain, transducer gain, and available gain are expanded by taking harmonic content into account. An example is provided showing how the expanded definitions of gain and nonlinear large-signal S -parameters allow one to examine the behavior of a nonlinear model by simply performing a harmonic balance simulation. Next, this thesis illustrates how nonlinear large-signal S -parameters can be used as a tool in the design process of a nonlinear circuit, specifically a single-diode 1–2 GHz frequency-doubler. For the case where a nonlinear model is not readily available, a method of extracting nonlinear large-signal S -parameters is developed using ANN models trained with multiple measurements made by a nonlinear vector network analyzer equipped with two sources. Finally, nonlinear large-signal S -parameters are compared to another form of nonlinear mapping, known as nonlinear scattering functions. The nonlinear large-signal S -parameters are shown to be more general.
机译：本文由两部分组成。第一部分涵盖了用于改进的矢量网络分析仪（VNA）校准的人工神经网络（ANN）建模。具体来说，基于测量的人工神经网络被用于对各种晶圆上和同轴矢量网络分析仪校准进行建模，包括直通-短载-直通（OSLT）和线反射匹配（LRM）。通过将训练误差确定为隐藏神经元数量和训练点数量的函数，可以对ANN进行灵敏度分析。然后，使用ANN建模的标准评估这些校准的各自精度。作为一项重要的研究结果，该博士论文表明，与使用校准的测量数据文件或等效电路模型相比，人工神经网络模型具有许多优势，即：它们不需要物理模型的大量细节和参数；由于只需要几个培训点就可以对标准模型进行准确建模，因此可以减少校准时间。 ANN模型描述比大型测量数据文件要紧凑得多。当有限的校准数据可用时，仅在几个测量点上训练的ANN模型比直接校准要精确得多。人工神经网络在存在噪声的情况下给出了优化的估计。以及ANN模型能够以测得的直流电阻稍微超出其训练范围的方式对负载进行精确建模。在本文的第二部分，针对稀疏输入，开发了用于非线性微波电路的新频域模型和品质因数。本节从一种方法开始，该方法用于通过引入用作相位参考的第三信号在两个谐波相关信号之间取比率时保持时不变相位关系。然后，作为另一项主要研究成果，该博士论文介绍了非线性大信号散射（ S ）参数，这是一种与入射和反射信号相关的新型频域映射。提出了非线性大信号 S 参数的一般形式。结果表明，在不存在非线性的情况下，它们可简化为经典的 S 参数。非线性大信号阻抗（ Z ）和导纳（ A ）参数，还引入了与不同表示形式有关的方程式。接下来，通过考虑谐波含量来扩展功率增益，换能器增益和可用增益的定义。提供了一个示例，说明了增益和非线性大信号 S 的扩展定义的方法-参数允许通过简单地执行谐波平衡仿真来检查非线性模型的行为。接下来，本文说明了如何使用非线性大信号 S 参数在非线性电路，特别是单二极管1-2 GHz倍频器的设计过程中用作工具。对于非线性模型不可用的情况，一种提取非线性大信号 S 的方法f> 参数是使用经过人工训练的ANN模型开发的，该模型经过多次测量，并由配备两个源的非线性矢量网络分析仪进行了测量。最后，将非线性大信号 S 参数与另一种形式的参数进行比较非线性映射，称为非线性散射函数。非线性大信号 S 参数显示得更通用。

著录项

  • 作者

    Jargon, Jeffrey Arendt.;

  • 作者单位

    University of Colorado at Boulder.;

  • 授予单位 University of Colorado at Boulder.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 175 p.
  • 总页数 175
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:44:50

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