Accuracy of Quasi-Monte Carlo technique in failure probability estimations

Noltsis, Michail; Weckx, Pieter; Rodopoulos, Dimitrios; Cathoor, F.; Soudris, Dimitrios

doi:10.1109/icicdt.2016.7542044

Cited by 2 publications

(1 citation statement)

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“…This fitting process of the latter methodology inevitably introduces errors hence, the objective to capture failure probability values with a higher accuracy still remains. Although approaches like the Quasi-Monte Carlo technique have also been proposed, their implementation is still of questionable accuracy, especially when focusing on aggressively downscaled devices [7]. Then, the Importance Sampling technique can be used for such estimations, however, the accuracy of this approach heavily depends on the alternate distribution which ideally should be close to the final one.…”

Section: Introductionmentioning

confidence: 99%

Failure probability of a FinFET-based SRAM cell utilizing the most probable failure point

et al. 2019

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Application requirements along with the unceasing demand for ever-higher scale of device integration, has driven technology towards an aggressive downscaling of transistor dimensions. This development is confronted with variability challenges, mainly the growing susceptibility to time-zero and timedependent variations. To model such threats and estimate their impact on a system's operation, the reliability community has focused largely on Monte Carlo-based simulations and methodologies. When assessing yield and failure probability metrics, an essential part of the process is to accurately capture the lower tail of a distribution. Nevertheless, the incapability of widely-used Monte Carlo techniques to achieve such a task has been identified and recently, state-of-the-art methodologies focusing on a Most Probable Failure Point (MPFP) approach have been presented. However, to strictly prove the correctness of such approaches and utilize them on large scale, an examination of the concavity of the space under study is essential. To this end, we develop an MPFP methodology to estimate the failure probability of a FinFET-based SRAM cell, studying the concavity of the Static Noise Margin (SNM) while comparing the results against a Monte Carlo methodology.

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Section: Introductionmentioning

confidence: 99%