Exploiting Extended Krylov Subspace for the Reduction of Regular and Singular Circuit Models

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 behaviour. 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 grid benchmarks demonstrate that our method achieves an error reduction up to 83.69% over a standard Krylov subspace technique.