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

Authors:
Yehia Massoud;Sami Kirolos;Kartik Mohanram
Affiliations:
Department of Electrical and Computer Engineering, Rice University, Houston, USA 77005;Department of Electrical and Computer Engineering, Rice University, Houston, USA 77005;Department of Electrical and Computer Engineering, Rice University, Houston, USA 77005
Venue:
Analog Integrated Circuits and Signal Processing
Year:
2009

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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.