AMOR: An efficient aggregating based model order reduction method for many-terminal interconnect circuits

Yangfeng Su, Fan Yang, Xuan Zeng
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引用次数: 14

Abstract

In this paper, we propose an efficient Aggregating based Model Order Reduction method (AMOR) for many-terminal interconnect circuits. The proposed AMOR method is based on the observation that those adjacent nodes of interconnect circuits with almost the same voltage can be aggregated together as a “super node”. Motivated by such an idea, we propose an efficient spectral partition algorithm in AMOR method to partition the nodes into groups with almost the same voltages. The reduced-order models are then obtained by aggregating the adjacent nodes within the same groups together as “super nodes” in AMOR method. The efficiency of AMOR method is not limited by the numbers of the terminals of the networks. Moreover, noticing that the aggregating procedure can be regarded as mapping the original problem into a coarse-grid problem in multigrid method, we propose a computation-efficient smoothing procedure to further improve the simulation accuracy of the reduced-order models. With such a strategy, the simulation accuracy of the reduced-order models can always be guaranteed. Numerical results have demonstrated that, without the smoothing procedure, the reduced-order models obtained by AMOR can still achieve higher simulation efficiency in terms of accuracy and CPU time than the reduced-order models obtained by the existing elimination based methods. With the smoothing procedure, the simulation accuracy of the reduced-order models can further be improved with several iterations.
多端互连电路中一种有效的基于聚合的模型降阶方法
本文提出了一种基于聚合的多端互连电路模型降阶方法(AMOR)。提出的AMOR方法是基于观察到互连电路中具有几乎相同电压的相邻节点可以聚集在一起作为“超级节点”。基于这种思想,我们提出了一种高效的AMOR方法中的频谱划分算法,将节点划分为电压几乎相同的组。然后通过AMOR方法将同一组内的相邻节点作为“超级节点”聚集在一起,得到降阶模型。AMOR方法的效率不受网络终端数量的限制。此外,注意到聚合过程可以看作是将多网格方法中的原始问题映射为粗网格问题,我们提出了一种计算效率高的平滑过程,以进一步提高降阶模型的仿真精度。采用这种策略,总能保证降阶模型的仿真精度。数值结果表明,在不进行平滑处理的情况下,AMOR方法得到的降阶模型在精度和CPU时间上仍然比现有基于消去的方法得到的降阶模型具有更高的仿真效率。采用平滑处理后,可通过多次迭代进一步提高降阶模型的仿真精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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