Zonotope-based nonlinear model order reduction for fast performance bound analysis of analog circuits with multiple-interval-valued parameter variations

It is challenging to efficiently evaluate performance bound of high-precision analog circuits with multiple parameter variations at nano-scale. In this paper, a nonlinear model order reduction is proposed to deploy zonotope-based model for multiple-interval-valued parameter variations. As such, one can have a zonotope-based reachability analysis to generate a set of trajectories with performance bound defined. By further constructing local parameterized subspaces to approximate a number of zonotopes along the set of trajectories, one can perform nonlinear model order reduction to generate the performance bound under parameter variations. As shown by numerical experiments, the zonotope-based nonlinear macromodeling by order of 19 achieves up to 500× speedup when compared to Monte Carlo simulations of the original model; and up to 50% smaller error when compared to previous parameterized nonlinear macromodeling under the same order.