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Model order reduction (MOR) is an important step in the design process ofintegrated circuits. Specifically, the electromagnetic models extracted frommodern complex designs result in a large number of passive elements thatintroduce limitations in the simulation process. MOR techniques based onbalanced truncation (BT) can overcome these limitations by producing compactreduced-order models (ROMs) that approximate the behavior of the originalmodels at the input/output ports. In this paper, we present a low-rank BTmethod that exploits the extended Krylov subspace and efficient implementationtechniques for the reduction of large-scale models. Experimental evaluation ona diverse set of analog and mixed-signal circuits with millions of elementsindicates that up to x5.5 smaller ROMs can be produced with similar accuracy toANSYS RaptorX ROMs.

Reduction of large-scale RLCk models via low-rank balanced truncation