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Record W1991075467 · doi:10.1109/tvlsi.2011.2178046

Statistical SRAM Read Access Yield Improvement Using Negative Capacitance Circuits

2011· article· en· W1991075467 on OpenAlexaff
Hassan Mostafa, Mohab Anis, M.I. Elmasry

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStatic random-access memoryAccess timeCMOSCapacitanceElectronic circuitElectronic engineeringComputer scienceProcess variationNoise marginParasitic capacitanceBlock (permutation group theory)ChipComputer hardwareElectrical engineeringEngineeringVoltageTransistorMathematics

Abstract

fetched live from OpenAlex

SRAM has become the dominant block in modern ICs and constitutes more than 50% of the die area. The increase of process variations with continued CMOS technology scaling is considered one of the major challenges for SRAM designers. This process variations increase causes the SRAM cells to functionally fail and reduces the chip functional yield considering the static noise margin stability failures (i.e., cell flips when accessed), write failures (i.e., cell is not written within the write window), and read access failures (i.e., incorrect read operation). In this paper, novel negative capacitance circuits are developed, for the first time, to statistically improve the SRAM read access yield under process variations by reducing the bitlines parasitic capacitance. Post layout simulation results, referring to an industrial hardware-calibrated TSMC 65-nm CMOS technology, show that the adoption of the negative capacitance circuit to a 512 SRAM cells column is capable of improving the read access yield from 61.9% to 100%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.254
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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