Efficient parametric yield extraction for multiple correlated non-normal performance distributions of Analog/RF circuits
Xin Li,Larry Pileggi +1 more
- 04 Jun 2007
- pp 928-933
TL;DR: An efficient numerical algorithm to estimate the parametric yield of analog/RF circuits with consideration of large-scale process variations is proposed, especially developed to handle multiple correlated non-Normal performance distributions, thereby providing better accuracy than other traditional techniques.
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Abstract: In this paper we propose an efficient numerical algorithm to estimate the parametric yield of analog/RF circuits with consideration of large-scale process variations. Unlike many traditional approaches that assume Normal performance distributions, the proposed approach is especially developed to handle multiple correlated non-Normal performance distributions, thereby providing better accuracy than other traditional techniques. Starting from a set of quadratic performance models, the proposed parametric yield extraction conceptually maps multiple correlated performance constraints to a single auxiliary constraint using a MAX(ldr) operator. As such, the parametric yield is uniquely determined by the probability distribution of the auxiliary constraint and, therefore, can be easily computed. In addition, a novel second-order statistical Taylor expansion is proposed for an analytical MAX(ldr) approximation, facilitating fast yield estimation. Our numerical examples in a commercial BiCMOS process demonstrate that the proposed algorithm provides 2~3times error reduction compared with a Normal- distribution-based method, while achieving orders of magnitude more efficiency than the Monte Carlo analysis with 104 samples.
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Defining Statistical Timing Sensitivity for Logic Circuits With Large-Scale Process and Environmental Variations
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Hongzhou Liu,Amith Singhee,Rob A. Rutenbar,L.R. Carley +3 more
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- 10 Nov 1996
TL;DR: A methodology for hierarchical statistical circuit characterization which does not rely upon circuit-level Monte Carlo simulation is presented and permits the statistical characterization of large analog and mixed-signal systems.
Defining statistical sensitivity for timing optimization of logic circuits with large-scale process and environmental variations
Xin Li,Jiayong Le,Mustafa Celik,Larry Pileggi +3 more
- 31 May 2005
TL;DR: Why the traditional concepts of slack and critical path become ineffective under large-scale variations are demonstrated, and a novel sensitivity-based metric to assess the "criticality" of each path and/or arc in the statistical timing graph is proposed.