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Record W2101088248 · doi:10.1109/tcsi.2010.2052490

A Switching-Based Phase Noise Model for CMOS Ring Oscillators Based on Multiple Thresholds Crossing

2010· article· en· W2101088248 on OpenAlexaff
Bosco Leung

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsJitterTriodeRing oscillatorZero crossingPhase noiseSwingTransistorCMOSPhysicsInverterResistorElectrical engineeringVoltageElectronic engineeringOptoelectronicsEngineeringAcousticsOptics

Abstract

fetched live from OpenAlex

For a ring oscillator with an arbitrary voltage swing, core transistors in delay cells typically move between saturation and triode region This can result in the overall timing jitter being dominated by timing jitter accumulated within a particular region. Based on multiple thresholds crossing concept, a new and more accurate way of handling such region change is developed. Specifically any crossing between two such regions, prior to the actual crossing of the threshold that triggers the next stage delay cell, is treated as an internal threshold crossing. The timing jitter is then the sum (in the rms sense) of the timing jitter accumulated across multiple thresholds crossing. The model agrees to within 2 dB with measurements, on differential pair based (both replica bias and physical resistor load) and current starved inverter based ring oscillators, fabricated in CMOS. Design insights from the model show that, for a differential pair ring oscillator that is originally designed with a given voltage swing such that the input transistors can be in triode, if voltage swing is reduced so that input transistors just do not go into triode, phase noise can be improved. A 7-dB phase noise improvement on an example design using a replica bias differential ring oscillator, based on this insight, is demonstrated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations26
Published2010
Admission routes1
Has abstractyes

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