With a short product cycle as we see today, fast and accurate modeling methods are becoming crucial for the development of new generation of electronics devices. Furthermore, increased complexity in circuitry and integration compounds design iteration and the associated, high-dimensional sensitivity analysis and performance optimization studies. Therefore, black-box surrogate models replacing the actual circuitry offer an attractive alternative for more efficient design iteration, optimization, and even direct Monte Carlo analysis. In this article, surrogate models built using nonparametric Gaussian process (GP) are presented. A robust framework based on probabilistic programming is used for training GP models. Other methods, such as partial least-square regression, support vector regression, and polynomial chaos, are used to compare with the performance of GP. Three design applications, a high-speed channel, a millimeter-wave filter, and a low-noise amplifier are used to demonstrate the robustness of the proposed GP-based surrogate models.

中文翻译：

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信号完整性和微波电路应用的替代建模方法的比较研究

我们今天看到的产品周期很短，快速准确的建模方法对于新一代电子设备的开发变得至关重要。此外，电路和集成化合物设计迭代以及相关的高维灵敏度分析和性能优化研究的复杂性增加。因此，替代实际电路的黑盒代理模型为更高效的设计迭代、优化甚至直接蒙特卡罗分析提供了一种有吸引力的替代方案。在本文中，介绍了使用非参数高斯过程 (GP) 构建的替代模型。基于概率编程的健壮框架用于训练 GP 模型。其他方法，如偏最小二乘回归、支持向量回归和多项式混沌，用来与GP的性能进行比较。三个设计应用、一个高速通道、一个毫米波滤波器和一个低噪声放大器用于证明所提出的基于 GP 的替代模型的稳健性。