On symbolic model order reduction
Symbolic model order reduction (SMOR) is a macromodeling technique that generates reduced-order models while retaining the parameters in the original models. Such symbolic reduced-order models can be repeatedly simulated with a greater efficiency for varying model parameters. Although the modelorder-reduction concept has been extensively developed in literature and widely applied in a variety of problems, model order reduction from a symbolic perspective has not been well studied. Several methods developed in this paper include symbol isolation, nominal projection, and first-order approximation. These methods can be applied to models having only a few parametric elements and to models having many symbolic elements. Of special practical interest are models that have slightly varying parameters such as process related variations, for which efficient reduction procedures can be developed. Each technique proposed in this paper has been tested by circuit examples. Experiments show that the proposed methods are efficient and effective for many circuit problems.
symbolic model model order reduction symbol isolation nominal projection first-order approximation arnoldi algorithm krylov subspace moment matching monte carlo simulation