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Record W1966187096 · doi:10.1109/newcas.2012.6329037

Ultra low leakage structures for logic circuits using symmetric and asymmetric FinFETs

2012· article· en· W1966187096 on OpenAlexaff
Farid Moshgelani, D. Al-Khalili, C. Rozon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTransistorLogic gateLeakage (economics)Electronic circuitElectronic engineeringPass transistor logicLogic levelVoltageElectrical engineeringComputer scienceMaterials scienceTopology (electrical circuits)Engineering

Abstract

fetched live from OpenAlex

In this paper, FinFET devices are analyzed with emphasis on sub-threshold leakage current control. This is achieved through proper biasing of the back gate, and through use of asymmetric work functions for the four terminal FinFET devices. We are also examining various transistor configurations and circuit topologies for logic gates using both symmetric and asymmetric work function transistors. Based on extensive characterization data, the logic gates using symmetric devices with one additional supply voltage have the best tradeoff between leakage current and performance. However, with asymmetric devices the leakage current drops by an average of 95% with degradation in delay of 8%. For a carry generation complex gate, our configuration, using symmetric devices, both leakage current and delay are improved by 35% and 47% respectively compared to results in the literature. Using asymmetric devices without additional supply achieves average improvements in leakage and delay of 81% and 5% respectively. All simulations are based on 25nm FinFET technology using the University of Florida UFDG model.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.257
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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