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, the efficacy of these methods based on the ordinary Krylov subspace is usually unsatisfactory to approximate the original behaviour . In this paper, we present a moment matching method based on the extended Krylov subspace combined with the superposition property that can handle large input models and generate more accurate and efficient reduced-order models for circuit simulation methods. The accuracy and efficiency of the proposed method is verified using industrial power grid benchmarks .
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 .
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