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Record W4411551353 · doi:10.1109/tcsii.2025.3582500

A Mutually Coupled High Speed Multi-Phase Clock Distribution Scheme for Improved Signal Integrity

2025· article· en· W4411551353 on OpenAlexaff
K. Cyril Baby

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsScheme (mathematics)SIGNAL (programming language)Phase (matter)Computer scienceSignal integrityClock signalElectronic engineeringPhysicsTelecommunicationsMathematicsEngineering

Abstract

fetched live from OpenAlex

An efficient clock distribution scheme is very important in high-speed interface designs as it directly impacts power, performance and area (PPA) of overall high-speed link. Ever increasing interface speed has resulted in multi-phase clock distributions (e.g., half-rate, quarter-rate architecture) becoming a reality over single / complementary phase clock distribution for performance reasons. Conventional techniques of clock distribution often involve distributing multi-phase clocks in pairs of complementary clock phases with proper shields around these phases to avoid coupling with other non-complementary clock phases of multi-phase clock distribution. However, these schemes suffer heavily when speeds/datarates and routing length of clock distribution increase. A mutually coupled multi-phase clock distribution scheme is proposed that overcomes signal integrity issues observed in the conventional multi-phase clock distribution scheme. Two schemes, correct by design (CD) and correct by layout (CL) are proposed to boost performance over conventional methods. Mutual coupling is effectively used between N-phase clocks and matched clocks are distributed with minimal/zero power overhead. Implemented in 7nm FinFET technology, the proposed schemes improve clock swings by 4.75%-8%, random jitter (RJ) by 4%-10%, rise/fall times by 10%-23% and achieve perfect matching between N phases of the clock.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.281
Teacher spread0.257 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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