During the past decade, Model Order Reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on moment-matching are well established due to their simplicity and computational performance in the reduction process. However, moment-matching methods based on the ordinary Krylov subspace are usually inadequate to accurately approximate the original circuit behavior. In this paper, we present a moment-matching method which is based on the extended Krylov subspace and exploits the superposition property in order to deal with many terminals. The proposed method can handle large-scale regular and singular circuits and generate accurate and efficient reduced-order models for circuit simulation. Experimental results on industrial IBM power grids demonstrate that our method achieves an error reduction up to 83.69% over a standard Krylov subspace method.

中文翻译：

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利用扩展的 Krylov 子空间来减少规则和奇异电路模型

在过去十年中，模型降阶 (MOR) 已成为高效仿真大型电路模型的关键推动因素。基于力矩匹配的 MOR 技术由于其在减少过程中的简单性和计算性能而被很好地建立。然而，基于普通 Krylov 子空间的矩匹配方法通常不足以准确地近似原始电路行为。在本文中，我们提出了一种基于扩展 Krylov 子空间并利用叠加特性来处理许多终端的矩匹配方法。所提出的方法可以处理大规模的规则和奇异电路，并为电路仿真生成准确有效的降阶模型。