A fast analog mismatch analysis by an incremental and stochastic trajectory piecewise linear macromodel

  • Authors:
  • Hao Yu;Xuexin Liu;Hai Wang;Sheldon X.-D. Tan

  • Affiliations:
  • Nanyang Technological University, Singapore;University of California, Riverside, CA;University of California, Riverside, CA;University of California, Riverside, CA

  • Venue:
  • Proceedings of the 2010 Asia and South Pacific Design Automation Conference
  • Year:
  • 2010

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Abstract

To cope with an increasing complexity when analyzing analog mismatch in sub-90nm designs, this paper presents a fast non-Monte-Carlo method to calculate mismatch in time domain. The local random mismatch is described by a noise source with an explicit dependence on geometric parameters, and is further expanded by stochastic orthogonal polynomials (SOPs). This forms a stochastic differential-algebra-equation (SDAE). To deal with large-scale problems, the SDAE is linearized at a number of snapshots along the nominal transient trajectory, and hence is naturally embedded into a trajectory-piecewise-linear (TPWL) macromodeling. The TPWL is improved with a novel incremental aggregation of sub-spaces identified at those snapshots. Experiments show that the proposed method, is TPWL, is hundreds of times faster than Monte-Carlo method with a similar accuracy. In addition, our macromodel further reduces runtime by up to 25X, and is faster to build and more accurate to simulate compared to existing approaches.