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Record W3209762372 · doi:10.1109/ojsscs.2021.3117930

Design Methodologies for Low-Jitter CMOS Clock Distribution

2021· article· en· W3209762372 on OpenAlexafffund
Xunjun Mo, Jiaqi Wu, Nijwm Wary, Anthony Chan Carusone

Bibliographic record

VenueIEEE Open Journal of the Solid-State Circuits Society · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsHuawei Technologies (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaHuawei Technologies
KeywordsJitterCMOSCPU multiplierClock gatingElectronic engineeringClock skewClock domain crossingComputer scienceDigital clock managerWirelineElectronic circuitConvertersClock signalElectrical engineeringSynchronous circuitEngineeringVoltageTelecommunicationsWireless

Abstract

fetched live from OpenAlex

Clock jitter negatively affects the performance of sampling circuits such as high-speed wireline transceivers and data converters. With CMOS buffers being increasingly used for the distribution of precise clocks in advanced technologies, it is important to understand their limitations and explore design tradeoffs. This tutorial provides quantitative analyses of the main sources of jitter in CMOS clock distribution: power supply induced jitter, jitter generation, and jitter amplification. Minimizing the number of buffers along the clock distribution network while still maintaining fast rise-fall times and ensuring proper settling of all clock waveforms will minimize the impact of all jitter sources. Following these guidelines can simultaneously reduce power supply noise sensitivity and power consumption of the clock distribution circuits. These conclusions are backed up by simulation and measurement results of two 16-nm FinFET clock distribution networks.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.089
GPT teacher head0.341
Teacher spread0.252 · 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
GenreMethods

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

Citations13
Published2021
Admission routes2
Has abstractyes

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Same venueIEEE Open Journal of the Solid-State Circuits SocietySame topicAdvancements in PLL and VCO TechnologiesFrench-language works237,207