1 research outputs found
Reduction of large-scale RLCk models via low-rank balanced truncation
Model order reduction (MOR) is an important step in the design process of
integrated circuits. Specifically, the electromagnetic models extracted from
modern complex designs result in a large number of passive elements that
introduce limitations in the simulation process. MOR techniques based on
balanced truncation (BT) can overcome these limitations by producing compact
reduced-order models (ROMs) that approximate the behavior of the original
models at the input/output ports. In this paper, we present a low-rank BT
method that exploits the extended Krylov subspace and efficient implementation
techniques for the reduction of large-scale models. Experimental evaluation on
a diverse set of analog and mixed-signal circuits with millions of elements
indicates that up to x5.5 smaller ROMs can be produced with similar accuracy to
ANSYS RaptorX ROMs