Advanced FinFET SRAMs undergo reliability degradation due to various front-end and back-end wearout mechanisms. The design of reliable SRAMs benefits from accurate wearout models that are calibrated by accelerated test. With respect to testing, the accelerated conditions which can help separate the dominant wearout mechanisms related to circuit failure is crucial for model calibration and reliability prediction. In this paper, the estimation of optimal accelerated test regions for a 14nm FinFET SRAM under various wearout mechanisms is presented. The dominant regions for specific mechanisms are compared and analyzed for effective testing. It is observed that for our SRAM example circuit only bias temperature instability (BTI) and middle-of-line time-dependent dielectric breakdown (MTDDB) have test regions where their failures can be isolated, while the other mechanisms can’t be extracted individually due to acceptable regions’ overlap. Meanwhile, the SRAM cell activity distribution has a small influence on test regions and selectivity.
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Optimal Accelerated Test Regions for Time- Dependent Dielectric Breakdown Lifetime Parameters Estimation in FinFET Technology
This paper proposes a methodology to find optimal accelerated test regions for lifetime parameter estimation for not only the traditional reliability concern, frontend-of-line dielectric breakdown (FEOL TDDB), but also the newly emerging wearout mechanism, middle-of-line time dependent dielectric breakdown (MOL TDDB) in 14nm FinFET technology. The framework to find the optimal test regions is introduced; the error estimating methodology is discussed in detail. Three digital circuits are presented for evaluation and comparison. The optimal test regions depend on the circuit size as well as the types of standard cells in the circuits. To ensure accurate lifetime parameter estimation, both error from sampling and error from selectivity should be considered at the same time. As a general guideline, higher estimation accuracy will be achieved by testing gate TDDB lifetime parameters at higher voltages, while testing middle-of-line TDDB at higher temperatures.
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- Award ID(s):
- 1700914
- NSF-PAR ID:
- 10104486
- Date Published:
- Journal Name:
- Design of Circuits and Integrated Systems
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper presents a lifetime simulator for both Front-End-of-Line (FEOL) time dependent dielectric breakdown (TDDB) and the newly emerging Middle-of-Line (MOL) time dependent dielectric breakdown for FinFET technology. A lifetime assessment flow for digital circuits and microprocessors is proposed for the target wearout mechanisms, and its associated vulnerable feature extraction algorithms are discussed in detail. Our simulator incorporates the detailed electrical stress, temperature, linewidth of each standard cell within the digital circuit and microprocessor. Also, FEOL TDDB and MOL TDDB lifetimes are combined in the calculation of TDDB lifetime. Circuit designers can use the resulting lifetime information to guide and improve their circuits to make them more robust and reliable way.more » « less
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Accelerated lifetime tests are necessary for reliability evaluation of circuits and systems, but the parameters for choosing the test conditions are often unknown. Furthermore, reliability testing is generally performed on test structures that have different properties than actual circuits and systems, which may create inconsistencies in how circuits and systems work in reality. To combat this problem, we use ring oscillators, which are similar to circuits, based on the 14nm FinFET node as the circuit vehicle to extract wearout data. We explore the effects of testing time, sample size, and number of stages on the ability to detect failures for various test conditions, focusing on front-end time dependent dielectric breakdown, which is one of the most dominant wearout mechanisms.more » « less
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Abstract The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere‐ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (
day) MLD variability. Yet, many multivariate observations‐based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid‐latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction. -
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Abstract Aim Historical processes that shaped current diversity patterns of seaweeds remain poorly understood. Using Dictyotales, a globally distributed order of brown seaweeds as a model, we test if historical biogeographical and diversification patterns are comparable across clades. Dictyotales contain some 22 genera, three of which,
Dictyota ,Lobophora andPadina , are exceptionally diverse. Specifically, we test whether the evolutionary processes that shaped the latitudinal diversity patterns in these clades are in line with the tropical conservatism, out‐of‐the‐tropics or diversification rate hypotheses.Location Global coastal benthic marine environments.
Taxon Dictyotales (Phaeophyceae).
Methods Species diversity was inferred using DNA‐based species delineation, addressing cryptic diversity and circumventing taxonomic problems. A six‐gene time‐calibrated phylogeny, distribution data of 3,755 specimens and probabilistic modelling of geographical range evolution were used to infer historical biogeographical patterns. The phylogeny was tested against different trait‐dependent models to compare diversification rates for different geographical units as well as different thermal affinities.
Results Our results indicate that Dictyotales originated in the Middle Jurassic and reach a current peak of species diversity in the Central Indo‐Pacific. Ancestral range estimation points to a southern hemisphere origin of Dictyotales corresponding to the tropical southern Tethys Sea. Our results demonstrate that diversification rates were generally higher in tropical regions, but increased diversification rates in different clades are driven by different processes. Our results suggest that three major clades underwent a major diversification burst in the early Cenozoic, with
Dictyota andPadina expanding their distribution into temperate regions whileLobophora retained a predominantly tropical niche.Main conclusions Our results are consistent with both the tropical conservatism hypothesis, in which clades originate and remain in the tropics (
Lobophora ), and the out‐of‐the‐tropics scenario, where taxa originate and expand towards the temperate regions while preserving their presence in the tropics (Dictyota ,Padina ).
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DCIS.2018.8681497
