Scalable trajectory methods for on-demand analog macromodel extraction

Saurabh K. Tiwary, Rob A. Rutenbar
2005 Proceedings of the 42nd annual conference on Design automation - DAC '05  
Trajectory methods sample the state trajectory of a circuit as it simulates in the time domain, and build macromodels by reducing and interpolating among the linearizations created at a suitably spaced subset of the time points visited during training simulations. Unfortunately, moving from simple to industrial circuits requires more extensive training, which creates models too large to interpolate efficiently. To make trajectory methods practical, we describe a scalable interpolation
more » ... re, and the first implementation of a complete trajectory "infrastructure" inside a full SPICE engine. The approach supports arbitrarily large training runs, automatically prunes redundant trajectory samples, supports limited hierarchy, enables incremental macromodel updates, and gives 3-10X speedups for larger circuits.
doi:10.1145/1065579.1065686 dblp:conf/dac/TiwaryR05 fatcat:gtcqcbpnfzgmxph3huda43tffi