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Record W2080345293 · doi:10.1109/mwscas.2006.381795

An Area-Efficient CMOS Current-Mode Phase-Locked Loop

2006· article· en· W2080345293 on OpenAlexaff
Dominic DiClemente, Fei Yuan

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhase-locked loopDelay-locked loopCMOSPhase frequency detectorInductorVoltageElectronic engineeringControl theory (sociology)WaveformVoltage-controlled oscillatorLoop (graph theory)Current (fluid)EngineeringCharge pumpComputer scienceElectrical engineeringCapacitorPhase noise

Abstract

fetched live from OpenAlex

This paper introduces a new architecture of phase-locked loops called current-mode phase-locked loops. The proposed phase-locked loop removes the need for a charge pump and a RC loop filter as conventional voltage-mode phase- locked loops, eliminating the design difficulties including current mismatches of charge pumps and large silicon area requirement of the RC loop filter. Instead, the proposed current-mode phase- locked loop utilizes a current-mode RL loop filter with CMOS active inductors to provide the desired control current to the downstream current-controlled oscillator. A modified bang-bang phase/frequency detector that converts the phase difference between the incoming digital waveform and the local clock into a binary signal is proposed. A current-reuse current-controlled oscillator is also developed. Simulation results demonstrates that the proposed current-mode phase-locked loop provides an significantly reduced lock time as compared with the corresponding voltage-mode phase-locked loop with approximately 90% silicon area reduction.

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)0.000
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 score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.292
Teacher spread0.266 · 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

Citations4
Published2006
Admission routes1
Has abstractyes

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