基于非线性驱动的高效宏观模型的电源噪声建模

B. Mutnury, M. Swaminathan, J. Libous
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引用次数: 8

摘要

本文采用非线性数字驱动器的高效宏观模型对电源噪声进行了精确建模。有限时差近似样条函数建模技术在建模过程中同时考虑了驾驶员的静态和动态记忆特性。对于电源噪声分析,通过考虑电源电压/电流的前一个时间实例,将上述方法扩展到多个端口。当多个驱动器同时开关时,所讨论的方法可用于准确捕获同步开关噪声(SSN)和串扰等敏感效应。与晶体管电平驱动模型的比较研究表明,该方法的计算速度在10-40范围内,误差小于5%。对于高度非线性驱动,简要讨论了一种基于人工神经网络(ANN)的SSN捕获方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling of power supply noise using efficient macro-model of non-linear driver
In this paper, power supply noise is modeled accurately using efficient macro-models of nonlinear digital drivers. A spline function with finite time difference approximation modeling technique takes into account both the static and the dynamic memory characteristics of the driver during modeling. For power supply noise analysis, the above method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and crosstalk accurately, when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on artificial neural networks (ANN) is briefly discussed to capture SSN.
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