Variability aware performance evaluation of low power SRAM cell

Dsilva, Hansel; Pinto, J.; Elchidana, Arzan; Mande, Sudhakar

doi:10.1109/asqed.2013.6643584

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…[8] modeled the variability using threshold voltage as a source of variability to evaluate stability of 9T SRAMs for the 32nm node. [9] performed a full PVT analysis of 6T SRAM cells down to the 7nm node, however their variability modeling only considered two sources of mismatch: EOT and TOXE, which is inaccurate as the technology shrinks.…”

Section: Introductionmentioning

confidence: 99%

Four-injector variability modeling of FinFET predictive technology models

Royer

López‐Vallejo

Redondo

et al. 2014

2014 5th European Workshop on CMOS Variability (VARI)

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Abstract-The usual way of modeling variability using threshold voltage shift and drain current amplification is becoming inaccurate as new sources of variability appear in sub-22nm devices. In this work we apply the four-injector approach for variability modeling to the simulation of SRAMs with predictive technology models from 20nm down to 7nm nodes. We show that the SRAMs, designed following ITRS roadmap, present stability metrics higher by at least 20% compared to a classical variability modeling approach. Speed estimation is also pessimistic, whereas leakage is underestimated if sub-threshold slope and DIBL mismatch and their correlations with threshold voltage are not considered.

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Section: Introductionmentioning

confidence: 99%

Four-injector variability modeling of FinFET predictive technology models

Royer

López‐Vallejo

Redondo

et al. 2014

2014 5th European Workshop on CMOS Variability (VARI)

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show abstract

“…Other parameters, such as the oxide thickness can be varied in order to model variability. This was done by [DPEM13] to perform a full PVT analysis of 6T SRAM cells down to the 7nm node, varying the TOXE parameter value in the model cards of the predictive technology models. The effects of varying the oxide thickness of a 22 nm technology are shown in Figure 3.1c.…”

Section: Variability Modelingmentioning

confidence: 99%

Design and simulation of deep nanometer SRAM cells under energy, mismatch, and radiation constraints

Barrio¹

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Variability aware performance evaluation of low power SRAM cell

Cited by 2 publications

References 8 publications

Four-injector variability modeling of FinFET predictive technology models

Four-injector variability modeling of FinFET predictive technology models

Design and simulation of deep nanometer SRAM cells under energy, mismatch, and radiation constraints

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