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Record W3107870742 · doi:10.1049/pbcs064e_ch20

High-performance CMOS clock distribution

2020· book-chapter· en· W3107870742 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJitterCMOSInverterElectronic engineeringComputer scienceEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

In this chapter, we provided background on three major jitter sources in high performance CMOS clock distribution: power supply induced jitter (PSIJ), random jitter (RJ), and jitter amplification. We discussed how PSIJ is introduced in the CMOS inverter and its accumulation along a chain of buffers depending on the type of supply noise and its variation along the buffer chain. We also reviewed the analysis of RJ in the CMOS inverter. We described design tradeoffs to minimize both PSIJ and RJ in global clock distribution. We described linear models of jitter amplification, including the jitter impulse response (JIR) and jitter transfer function (JTF). Jitter amplification for buffers driving transmission -line interconnect was analyzed quantitatively, and simulations were used to develop insight. Design guidelines are also given for both cases. Finally, we discussed design considerations for jitter amplification in CMOS clock distribution. With the increasing use of CMOS circuits for high-performance clock distribution in advanced CMOS technologies, we believe the methods and guidelines in this chapter will prove ever more useful.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
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 integrity0.0010.001
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 head0.006
GPT teacher head0.158
Teacher spread0.152 · 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