Efficient statistical BJT modeling, why β is more than I/sub c//I/sub b/

McAndrew, Colin C.; Bates, J.; Ida, R.; Drennan, P.G.

doi:10.1109/bipol.1997.647349

Cited by 16 publications

(16 citation statements)

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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%

“…Notice that we focus on the stochastic variation, or referred as local mismatch in this paper. We illustrate the stochastic mismatch analysis using the CMOS transistors, but a similar approach can be extended to other types of transistors by the so-called propagation of variance (POV) method [McAndrew et al 1997;Drennan and McAndrew 2003].…”

Section: Stochastic Mismatch Analysismentioning

confidence: 99%

“…; and the parameter can be transistor width, length and oxide thickness, etc. Such a so-called stochastic sensitivity can be also understood based on the propagation of variance (POV) relationship [McAndrew et al 1997;Drennan and McAndrew 2003] below…”

Section: Stochastic Sensitivity 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%

See 3 more Smart Citations

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

Gong

Liu

et al. 2012

ACM Trans. Des. Autom. Electron. Syst.

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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: Stochastic Mismatch Analysismentioning

confidence: 99%

Section: Stochastic Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gong

Liu

et al. 2012

ACM Trans. Des. Autom. Electron. Syst.

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“…The backward propagation of variance (BPV) technique is a unified approach for physically based statistical modeling, for both global and local (mismatch) statistical variations. 4,5 In this article, we present recent extensions and enhancements to the BPV technique. We show how the assumption of a linear dependence of electrical performance on process parameters can be relaxed, and that correlations between different electrical performances can be explicitly included in the procedure.…”

mentioning

confidence: 99%

Extensions to Backward Propagation of Variance for Statistical Modeling

McAndrew¹,

Stevanović

et al. 2010

IEEE Des. Test. Comput.

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STATISTICAL SIMULATION IS IMPORTANT for the design of high-yield circuits, 1 and statistical simulation requires statistical models. Such models can be generated using numerical techniques such as principal component analysis or factor analysis. 2,3 However, physical models have proven substantially more accurate and efficient than strictly numerical models for retargeting statistical models (both for mean shifts and variance shifts in a process), predicting statistical models for a process based on previous generations of that process, and capturing unexpected or anomalous behavior in statistical variations over bias or geometry. The backward propagation of variance (BPV) technique is a unified approach for physically based statistical modeling, for both global and local (mismatch) statistical variations. 4,5 In this article, we present recent extensions and enhancements to the BPV technique. We show how the assumption of a linear dependence of electrical performance on process parameters can be relaxed, and that correlations between different electrical performances can be explicitly included in the procedure. We include measures of circuit performance as modeling targets, andshow that overspecifying the electrical performances to be modeled can improve overall statistical-modeling accuracy.

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