Towards improving simulation of analog circuits using model order reduction

Large analog circuit models are very expensive to evaluate and verify. New techniques are needed to shorten time-to-market and to reduce the cost of producing a correct analog integrated circuit. Model order reduction is an approach used to reduce the computational complexity of the mathematical model of a dynamical system, while capturing its main features. This technique can be used to reduce an analog circuit model while retaining its realistic behavior. In this paper, we present an approach to model order reduction of nonlinear analog circuits. We model the circuit using fuzzy differential equations and use qualitative simulation and K-means clustering to discretion efficiently its state space. Moreover, we use a conformance checking approach to refine model order reduction steps and guarantee simulation acceleration and accuracy. In order to illustrate the effectiveness of our method, we applied it to a transmission line with nonlinear diodes and a large nonlinear ring oscillator circuit. Experimental results show that our reduced models are more than one order of magnitude faster and accurate when compared to existing methods.