Laguerre-SVD reduced order modeling

Circuit simulation tasks, such as the accurate prediction of the behavior of large RLGC interconnects, generally requires the solution of very large linear networks. In recent years, this has led to the development of reduced order modeling technologies such as Pade via Lanczos (Feldmann and Freund, 1995), block Arnoldi (Boley, 1994) and passive reduced-order interconnect macromodeling (PRIMA) (Odabasioglu et al., 1998). In this paper, a reduced order modeling technique based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition via singular value decomposition is presented. The link with Pade approximation, the block Arnoldi algorithm and the singular value decomposition (SVD) (Golub and Van Loan, 1996) permits a simple and stable implementation of the algorithm.