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Record W2791338777 · doi:10.1109/tvlsi.2017.2788882

Zero-Power Feed-Forward Spur Cancelation for Supply-Regulated CMOS Ring PLLs

2018· article· en· W2791338777 on OpenAlexfundno aff
Pawan Agarwal, Jonghoon Kim, Partha Pratim Pande, Deukhyoun Heo

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsRing oscillatorSpurPhase-locked loopdBcCMOSVoltage-controlled oscillatorElectrical engineeringElectronic engineeringControl theory (sociology)EngineeringVoltagePhase noiseComputer science

Abstract

fetched live from OpenAlex

A new reference-spur cancelation technique is presented for supply-regulated ring-oscillator-based integer-N phaselocked loops (PLLs). A passive RC filter is used to implement a feed-forward (FF) spur-coupling path to perform spur cancelation at the PLL control signal. The proposed technique achieves a simulated spur cancelation of about 22 dB at the first spur harmonic. The simulated postcancelation spur value is -79 dBc for an oscillator gain of 0.1 GHz/V and -46 dBc for an oscillator gain of 6 GHz/V. Spur cancelation is also robust against large process, voltage, and temperature variations in the gain and bandwidth of the FF path. A 1-GHz integerN PLL prototype in a 65-nm CMOS process has a measured cancelation of 19.5 and 13 dB at the first and the second spur harmonic, respectively, with 320 μW of total power consumption. The PLL prototype has an oscillator gain of 1.5 GHz/V, which results in a postcancelation spur of -53 dBc. The proposed zero-power technique is suitable for low-power PLLs as it achieves a large spur cancelation without requiring any additional power consumption or calibration.

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 score0.917
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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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