Adaptive sampling for efficient failure probability analysis of SRAM cells

Jaffari, Javid; Anis, Mohab

doi:10.1145/1687399.1687515

Cited by 14 publications

(16 citation statements)

References 16 publications

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“…In this paper, we adopt the Gibbs sampling method [11], [15] from statistics to predict the failure probability of SRAM cells. Compared to other traditional techniques [5]- [9], Gibbs sampling provides two promising features. First, it can efficiently search the failure region and determine the indicator function I(x) on the fly.…”

Section: Gibbs Samplingmentioning

confidence: 99%

“…While these models offer great design insights to understand SRAM circuits, they may not accurately capture the circuit behavior due to various approximations that are made. Another possible approach for SRAM failure rate prediction is based on transistor-level simulation, including both Monte Carlo analysis [5]- [9] and deterministic failure region prediction [10].…”

Section: Introductionmentioning

confidence: 99%

“…To improve the sampling efficiency, a number of importance sampling methods have been proposed for fast SRAM failure rate prediction [5]- [9]. The key idea of importance sampling is to directly sample the failure region based on a distorted probability density function (PDF), instead of the original PDF of process variations.…”

Section: Introductionmentioning

confidence: 99%

“…To address this computational cost issue, importance sampling has been proposed to improve the efficiency of Monte Carlo analysis [5]- [9]. It aims to directly generate a large number of random samples in the failure region by using a distorted PDF q(x).…”

Section: Introductionmentioning

confidence: 99%

“…Most exiting importance sampling algorithms apply various heuristics to approximate the optimal PDF q OPT (x) [5]- [9]. In this paper, we propose a new Gibbs sampling method that adaptively samples the optimal PDF q OPT (x) without explicitly knowing the indicator function I(x).…”

Section: Introductionmentioning

confidence: 99%

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Efficient SRAM failure rate prediction via Gibbs sampling

Dong

2011

Proceedings of the 48th Design Automation Conference

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Statistical analysis of SRAM has emerged as a challenging issue because the failure rate of SRAM cells is extremely small. In this paper, we develop an efficient importance sampling algorithm to capture the rare failure event of SRAM cells. In particular, we adapt the Gibbs sampling technique from the statistics community to find the optimal probability distribution for importance sampling with minimum computational cost (i.e., a small number of transistor-level simulations). The proposed Gibbs sampling method applies an integrated optimization engine to adaptively explore the failure region by sampling a sequence of onedimensional probability distributions. Several implementation issues such as one-dimensional random sampling and starting point selection are carefully studied to make the Gibbs sampling method efficient and accurate for SRAM failure rate prediction. Our experimental results of a commercial 65nm SRAM cell demonstrate that the proposed Gibbs sampling method achieves 3~10u runtime speed-up over other state-of-the-art techniques without surrendering any accuracy.

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Section: Gibbs Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient SRAM failure rate prediction via Gibbs sampling

Dong

2011

Proceedings of the 48th Design Automation Conference

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A nonparametric importance sampling estimator for moment independent importance measures

Derennes

Mitard

Simatos

2019

Reliability Engineering & System Safety

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Moment independent importance measures have been proposed by E. Borgonovo [1] in order to alleviate some of the drawbacks of variance-based sensibility indices. They have gained increasing attention over the last years but their estimation remains a challenging issue. An effective estimation scheme in the case of correlated inputs, referred to as single-loop method, has been proposed by Wei et al. [2]. In this paper we show via simulation that this method may be inaccurate, making for instance 40% error in the simplest possible Gaussian case. We then propose a new estimation scheme which greatly improves the accuracy of the single-loop method, up to a factor 10 in some simple numerical examples. We prove that our estimator is strongly consistent and several simulation results are presented to demonstrate the advantages of the proposed method.

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Accurate Estimation of Data Retention Voltage (DRV) for a 6T SRAM Cell at 45 nm, 65 nm, and 130 nm Technology Nodes

Goyal¹,

Dasgupta

2023

IETE Journal of Research

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Adaptive sampling for efficient failure probability analysis of SRAM cells

Cited by 14 publications

References 16 publications

Efficient SRAM failure rate prediction via Gibbs sampling

Efficient SRAM failure rate prediction via Gibbs sampling

A nonparametric importance sampling estimator for moment independent importance measures

Accurate Estimation of Data Retention Voltage (DRV) for a 6T SRAM Cell at 45 nm, 65 nm, and 130 nm Technology Nodes

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