Design of ultra-low-leakage near-threshold dynamic circuits in nano CMOS for IoT applications

Chen, Bo‐Hao; Chou, Pei-Yuan; Fang, Yong; Yong, Lee-Kee; Lin, Tay-Jyi; Wang, Jinn‐Shyan

doi:10.1109/nano.2016.7751533

Cited by 3 publications

(1 citation statement)

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“…Low voltage design [1]- [4], including near/subthreshold design, has become an attractive solution for applications where performance is not the primary concern to save power. However, due to the small gate voltage drive of the transistors operating in the near/subthreshold voltage regime, the logic gates suffer from high sensitivity to process variation, thus leading to a wider spread in the statistical distribution of performance compared with the designs at super-threshold voltage [5].…”

Section: Introductionmentioning

confidence: 99%

An Analytical Gate Delay Model in Near/Subthreshold Domain Considering Process Variation

et al. 2019

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Voltage scaling technique is widely employed in state-of-the-art low power circuits with excellent power reduction. However, voltage scaling to sub-threshold (STV) and near-threshold (NTV) domain introduces performance degradation and high process variation sensitivity. Accurate modeling of the statistical characteristics especially the probability distribution function (PDF) and the cumulative distribution function (CDF) is urgently required with process variation consideration. In this paper, a novel analytical model is derived based on log-skew-normal (LSN) distribution to precisely evaluate the gate delay variation. The multi-variate threshold variation in stacked gates are modeled with a linear approximation method in delay distribution derivation. By applying the CDF of the proposed model, the maximum and minimum delay indicated by ±3σ percentile point can be calculated essentially different from the common method with much higher accuracy. Experimental results show the proposed model is highly fitted with Monte Carlo (MC) results for stochastic delay modeling of generic logic gates in near/subthreshold regime with less than 8% and 6% error in delay variability and ±3σ delay prediction, showing maximum accuracy improvement about 40 times compared to preproposal models.

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Section: Introductionmentioning

confidence: 99%