Linear robust control
An efficient Lyapunov equation-based approach for generating reduced-order models of interconnect
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Efficient model reduction of interconnect via approximate system gramians
ICCAD '99 Proceedings of the 1999 IEEE/ACM international conference on Computer-aided design
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient linear circuit analysis by Pade approximation via the Lanczos process
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A factorization-based framework for passivity-preserving model reduction of RLC systems
Proceedings of the 39th annual Design Automation Conference
Passivity-preserving model reduction via a computationally efficient project-and-balance scheme
Proceedings of the 41st annual Design Automation Conference
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
h2-norm optimal model reduction for large scale discrete dynamical MIMO systems
Journal of Computational and Applied Mathematics
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We present an efficient implementation of an approximate balanced truncation model reduction technique for general large-scale RLC systems, described by a state-space model where the "C" matrix in the time-domain modified nodal analysis (MNA) circuit equation "C\dot{x}=-Gx+Bu" is not necessarily invertible. The large sizes of the models that we consider make most implementations of the balance-and-truncate method impractical from the points of view of computational load and numerical conditioning. This motivates our use of Krylov subspace methods to directly compute approximate low-rank square roots of the Gramians of the original system. The approximate low-order general balanced and truncated model can then be constructed directly from these square roots. We demonstrate using three practical circuit examples that our new approach effectively gives approximate balanced and reduced order coordinates with little truncation error.