论文标题
利用扩展的Krylov子空间来减少常规电路模型
Exploiting Extended Krylov Subspace for the Reduction of Regular and Singular Circuit Models
论文作者
论文摘要
在过去的十年中，模型订单降低（MOR）已成为有效模拟大型电路模型的关键推动剂。基于力矩匹配的MOR技术在还原过程中的简单性和计算性能而建立了很好的确定。但是，基于普通的Krylov子空间的力矩匹配方法通常不足以准确近似原始电路行为。在本文中，我们提出了一种基于扩展的Krylov子空间，并利用叠加属性以处理许多终端的方法。所提出的方法可以处理大规模的常规和奇异电路，并为电路模拟生成准确有效的降低阶模型。工业IBM功率网格的实验结果表明，在标准的Krylov子空间方法上，我们的方法可实现高达83.69％的误差。
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.
