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Record W2121955149 · doi:10.1109/test.2001.966708

A synthesizable, fast and high-resolution timing measurement device using a component-invariant vernier delay line

2002· article· en· W2121955149 on OpenAlexafffund
A.H. Chan, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsVernier scaleJitterComputer scienceField-programmable gate arrayDelay calculationComputer hardwarePropagation delay

Abstract

fetched live from OpenAlex

In recent years, much effort has been placed on improving the performance of timing and jitter measurement devices using Delay Locked Loop (DLL) and Vernier Delay Line (VDL) techniques. However, these approaches require highly matched elements in order to reduce differential nonlinearity timing errors. In an attempt to reduce the requirement on element matching, a component-invariant VDL technique is proposed that will enable the measurement device to be synthesized from a register transfer level (RTL) description. Furthermore, as test time is an important consideration during a production test, a method is provided that reduces test time at the expense of more hardware. Experimental results on an FPGA implementation are provided as proof of concept. An IC prototype has also been designed and submitted for fabrication. Implementation details are provided in this paper.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.071
GPT teacher head0.234
Teacher spread0.163 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations54
Published2002
Admission routes2
Has abstractyes

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