Scalable trajectory methods for on-demand analog macromodel extraction

Saurabh K. Tiwary, Rob A. Rutenbar

Research output: Contribution to journalConference articlepeer-review


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 architecture, 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.

Original languageEnglish (US)
Article number25.3
Pages (from-to)403-408
Number of pages6
JournalProceedings - Design Automation Conference
StatePublished - Dec 1 2005
Event42nd Design Automation Conference, DAC 2005 - Anaheim, CA, United States
Duration: Jun 13 2005Jun 17 2005


  • Analog
  • Circuit
  • Macromodel
  • Trajectory method

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

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