SiSMA—A Tool for Efficient Analysis of Analog CMOS Integrated Circuits Affected by Device Mismatch

Biagetti, Giorgio; Orcioni, Simone; Turchetti, Claudio; Crippa, Paolo; Alessandrini, Michele

doi:10.1109/tcad.2003.822131

Cited by 21 publications

(18 citation statements)

References 27 publications

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“…We further review the existing works of mismatch analysis [Pelgrom et al 1989;McAndrew et al 1997;Drennan and McAndrew 2003;Biagetti et al 2004]. Notice that we focus on the stochastic variation, or referred as local mismatch in this paper.…”

Section: Stochastic Mismatch Analysismentioning

confidence: 99%

“…A robust design beyond 90nm is challenging due to PVT (Process, Voltage and Temperature) variations [Cox et al 1985;Pelgrom et al 1989;Lampaert et al 1995;McAndrew et al 1997;Schenkel et al 2001;Drennan and McAndrew 2003;Biagetti et al 2004;Vrudhula et al 2006;Kim et al 2007;Pileggi et al 2008;Nassif and Nowka 2010;Liu et al 2010;Gong et al 2011;Wang et al 2009;Gong et al 2010a;Gong et al 2010b;Gong et al 2009]. The sources of process variation can come from etching, lithography, polishing, and stress, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Many NMC methods [Oehm and Schumacher 1993;Biagetti et al 2004;Kim et al 2007] have been developed recently for stochastic variation (mismatch) analysis. [Oehm and Schumacher 1993] first calculated dc sensitivities with respect to small device-parameter perturbations and scaled them as desired mismatches.…”

Section: Introductionmentioning

confidence: 99%

“…The speed of these equivalent mismatch simulations is hundred times faster than the Monte-Carlo simulations but accuracy remains a concern. SiS-MA [Biagetti et al 2004] studied mismatch within the framework of the stochastic differential-algebra-equation (SDAE). The stochastic variational source is mapped into a noise current source introduced at dc, and the SDAE is solved similarly to deal with the transient noise [Demir et al 1994] by analyzing the correlation.…”

Section: Introductionmentioning

confidence: 99%

“…The stochastic variational source is mapped into a noise current source introduced at dc, and the SDAE is solved similarly to deal with the transient noise [Demir et al 1994] by analyzing the correlation. However, as [Biagetti et al 2004] introduced random variables into the DAE, it is unknown whether the derivative of SDAE is still continuous. Moreover, SiSMA only included stochastic current source during dc based on the assumption that magnitude of the stochastic mismatch is much smaller than the nominal case.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials

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Performance failure has become a significant threat to the reliability and robustness of analogue circuits. In this paper, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion under PVT variations and probabilistic distribution of transient response is solved. We further define performance yield and derive stochastic sensitivity for yield within the framework of SOP, and finally develop a gradient-based multi-objective optimization to improve yield while satisfying other performance constraints. Extensive experiments show that compared to Monte Carlo based yield estimation, our NMC method achieves up to 700X speedup and maintains 98% accuracy. Furthermore, multi-objective optimization not only improves yield by up to 95.3% with performance constraints, it also provides better efficiency than other existing methods.

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Section: Stochastic Mismatch Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Piecewise linear second moment statistical simulation of ICs affected by non‐linear statistical effects

Biagetti

Crippa

Curzi

et al. 2009

Circuit Theory & Apps

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SUMMARYThis paper presents a methodology for statistical simulation of non-linear integrated circuits affected by device mismatch. This simulation technique is aimed at helping designers maximize yield, since it can be orders of magnitude faster than other readily available methods, e.g. Monte Carlo. Statistical analysis is performed by modeling the electrical effects of tolerances by means of stochastic current or voltage sources, which depend on both device geometry and position across the die. They alter the behavior of both linear and non-linear components according to stochastic device models, which reflect the statistical properties of circuit devices up to the second order (i.e. covariance functions). DC, AC, and transient analyses are performed by means of the stochastic modified nodal analysis, using a piecewise linear stochastic technique with respect to the stochastic sources, around a few automatically selected points. Several experimental results on significant circuits, encompassing both the analog and the digital domains, prove the effectiveness of the proposed method.

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Biagetti

Crippa

Curzi

et al. 2010

Circuit Theory & Apps

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SiSMA—A Tool for Efficient Analysis of Analog CMOS Integrated Circuits Affected by Device Mismatch

Cited by 21 publications

References 27 publications

A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials

A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials

Piecewise linear second moment statistical simulation of ICs affected by non‐linear statistical effects

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