Analytical model-based technique for efficient evaluation of noise robustness considering parameter variations

Yehia Massoud; Sami Kirolos; Kartik Mohanram

doi:10.1007/s10470-008-9200-y

Analytical model-based technique for efficient evaluation of noise robustness considering parameter variations

Yehia Massoud, Sami Kirolos, Kartik Mohanram

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Scopus citations

Abstract

In this paper, we present an accurate method for analytical derivation of noise rejection curves (NRCs) and the associated noise susceptibility metric in the presence of variations in process and environmental parameters. The method involves modeling of the pull-up and pull-down resistances of combinational gates using approximated BSIM4 model-based device equations. Comparisons of the analytical model with circuit simulations show that the impact of parameter variations on the noise susceptibility is accurately captured by our model. The average (maximum) error associated with the noise susceptibility is found to be as low as 2.6% (6.7%). Our model can predict the noise susceptibility under parameter variations more than five orders of magnitude faster than circuit simulations, which makes it suitable for design optimization for noise robustness. © 2008 Springer Science+Business Media, LLC.

Original language	English (US)
Title of host publication	Analog Integrated Circuits and Signal Processing
Pages	27-34
Number of pages	8
DOIs	https://doi.org/10.1007/s10470-008-9200-y
State	Published - Aug 1 2009
Externally published	Yes

ASJC Scopus subject areas

Hardware and Architecture
Signal Processing
Surfaces, Coatings and Films

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10.1007/s10470-008-9200-y

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title = "Analytical model-based technique for efficient evaluation of noise robustness considering parameter variations",

abstract = "In this paper, we present an accurate method for analytical derivation of noise rejection curves (NRCs) and the associated noise susceptibility metric in the presence of variations in process and environmental parameters. The method involves modeling of the pull-up and pull-down resistances of combinational gates using approximated BSIM4 model-based device equations. Comparisons of the analytical model with circuit simulations show that the impact of parameter variations on the noise susceptibility is accurately captured by our model. The average (maximum) error associated with the noise susceptibility is found to be as low as 2.6% (6.7%). Our model can predict the noise susceptibility under parameter variations more than five orders of magnitude faster than circuit simulations, which makes it suitable for design optimization for noise robustness. {\textcopyright} 2008 Springer Science+Business Media, LLC.",

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AU - Kirolos, Sami

AU - Mohanram, Kartik

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PY - 2009/8/1

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N2 - In this paper, we present an accurate method for analytical derivation of noise rejection curves (NRCs) and the associated noise susceptibility metric in the presence of variations in process and environmental parameters. The method involves modeling of the pull-up and pull-down resistances of combinational gates using approximated BSIM4 model-based device equations. Comparisons of the analytical model with circuit simulations show that the impact of parameter variations on the noise susceptibility is accurately captured by our model. The average (maximum) error associated with the noise susceptibility is found to be as low as 2.6% (6.7%). Our model can predict the noise susceptibility under parameter variations more than five orders of magnitude faster than circuit simulations, which makes it suitable for design optimization for noise robustness. © 2008 Springer Science+Business Media, LLC.

AB - In this paper, we present an accurate method for analytical derivation of noise rejection curves (NRCs) and the associated noise susceptibility metric in the presence of variations in process and environmental parameters. The method involves modeling of the pull-up and pull-down resistances of combinational gates using approximated BSIM4 model-based device equations. Comparisons of the analytical model with circuit simulations show that the impact of parameter variations on the noise susceptibility is accurately captured by our model. The average (maximum) error associated with the noise susceptibility is found to be as low as 2.6% (6.7%). Our model can predict the noise susceptibility under parameter variations more than five orders of magnitude faster than circuit simulations, which makes it suitable for design optimization for noise robustness. © 2008 Springer Science+Business Media, LLC.

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M3 - Conference contribution

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BT - Analog Integrated Circuits and Signal Processing

ER -

Analytical model-based technique for efficient evaluation of noise robustness considering parameter variations

Abstract

ASJC Scopus subject areas

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