MétaCan
Menu
Back to cohort
Record W1964387409 · doi:10.1109/mwscas.2010.5548797

A comparative study of lock range of injection-locked active-inductor oscillators

2010· article· en· W1964387409 on OpenAlexafffund
Yushi Zhou, Fei Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsdBcPhase noiseInductorInjection lockingElectronic oscillatorPhase-locked loopVoltage-controlled oscillatorPhysicsVackář oscillatorOptoelectronicsMaterials scienceElectrical engineeringEngineeringLocal oscillatorVoltageOptics

Abstract

fetched live from OpenAlex

This paper presents the first study of the locking mechanism and lock range of injection-locked active inductor oscillators. A comparable study on the phase noise and lock range of active inductor oscillators and corresponding LC oscillators is carried out. Two 2.4 GHz oscillators, one active inductor oscillator and the other passive LC oscillator, are designed in IBM 0.13 μm 1.2 V CMOS technology and analyzed using Spectre using BSIM4 device models. Simulation results demonstrate that the phase noise of the active inductor oscillator is -70.45 dBc/Hz at 1 MHz frequency offset without locking and -117.9 dBc/Hz when locked to an external 2.4 GHz reference of phase noise -126.6 dBc/Hz. The phase noise of the LC oscillator is -105.1 dBc/Hz without locking and -126.1 dBc/Hz when locked. The upper and lower bounds of the frequency of the locking signal at which the injection-locked LC oscillator is locked are 2.4+0.02 GHz and 2.4-0.02 GHz, respectively where those of the injection-locked active inductor oscillator are 2.4+0.24 GHz and 2.4-0.12 GHz, respectively. The lock range of the injection-locked active inductor oscillator is 9 times that of the corresponding injection-locked passive LC oscillator with comparable phase noise.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.613

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 communication0.0000.000
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2010
Admission routes2
Has abstractyes

Explore more

Same topicRadio Frequency Integrated Circuit DesignFrench-language works237,207