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Decentralized and Passive Model Order Reduction of Linear Networks With Massive Ports

机译:具有大量端口的线性网络的分散式和被动式模型降阶

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It is well known that model order reduction for circuits with many terminals remains a challenging problem. One reason is that existing approaches are based on a centralized framework, in which each input-output pair is implicitly assumed to be equally interacted and the matrix-valued transfer function is assumed to be fully populated. In this paper, we attempt to address this long-standing problem using a decentralized model order reduction scheme, in which a multi-input multi-output system is decoupled into a number of subsystems and each subsystem corresponds to one output and several dominant inputs. The decoupling process is based on the relative gain array, which measures the degree of interaction of each input-output pair. For each decoupled subsystem, passive reduction can be easily achieved using existing reduction techniques. The proposed method is suitable for resistance-dominant interconnects such as on-chip power grids, substrate planes where extremely compact models can be obtained. Simulation results demonstrate the advantage of the proposed method compared to the existing approaches.
机译:众所周知,对于具有许多端子的电路,模型降阶仍然是一个难题。原因之一是现有方法基于集中式框架,其中每个输入-输出对被隐式假定为相等交互,并且矩阵值传递函数被假定为完全填充。在本文中,我们尝试使用分散的模型降阶方案来解决这个长期存在的问题,在该方案中,多输入多输出系统被解耦为多个子系统,每个子系统对应一个输出和几个主要输入。去耦过程基于相对增益阵列,该相对增益阵列测量每个输入输出对之间的相互作用程度。对于每个解耦的子系统,可以使用现有的还原技术轻松实现被动还原。所提出的方法适用于以电阻为主导的互连,例如片上电网,可以获得极其紧凑模型的基板平面。仿真结果证明了该方法与现有方法相比的优势。

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