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Record W2133908849 · doi:10.1109/iscas.1997.608710

Time delay measurement methods for integrated transmission lines and high speed cell characterization

2002· article· en· W2133908849 on OpenAlexaff
F. Pera, Yvon Savaria, Guy Bois

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsInterconnectionTransmission lineElectronic engineeringComputer scienceElectric power transmissionCMOSIntegrated circuitMeasure (data warehouse)Propagation delayTransmission (telecommunications)Electronic circuitCapacitive sensingEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In high speed integrated circuits, the time delays on interconnects become critical. An approximate solution based on capacitive and RC models for simulation is often not appropriate to describe critical paths where delays under 100 ps are to be considered. In such cases, long interconnections should be considered as transmission lines placed over a highly dispersive material. We developed new models for accurate simulation of interconnect lines on semiconductor substrate, and this paper deals with an experimental method to validate such models under real conditions. This paper presents new methods to measure the delays between two interconnected points, where the finite size of the buffer and transmission line parameters are considered. The proposed method was implemented in a CMOS demonstrator integrated circuit based on a sea-of-gate structure. This allows one to study various interconnection configurations which aim at improving the propagation of high speed signals The method can also be extended to on chip dynamic characterization of various complex cells operating at high speed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.241
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.249
Teacher spread0.215 · 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 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".

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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