Efficient Reduced-Order Macromodels of Massively Coupled Interconnect Structures via Clustering

In this paper, a novel algorithm for creating efficient reduced-order macromodels from massively coupled interconnect structures is described. The new algorithm addresses the difficulty associated with the reduction of networks with a large number of input/output terminals, that often results in large and dense reduced-order models. Application of the proposed reduction algorithm leads to reduced-order models that are sparse and block-diagonal in nature. An additional advantage of the proposed algorithm is that it does not assume any correlation between the responses at ports and thereby overcomes the accuracy degradation that is normally associated with the existing singular value decomposition based terminal reduction techniques. Also, the presented algorithm is highly suited for multithreading implementation and thus facilitates parallel transient simulation. Validity and efficiency of the proposed algorithm are demonstrated through computational results.