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Record W4392648083 · doi:10.1063/5.0195126

Low phase noise microwave oscillator based on gain driven polariton

2024· article· en· W4392648083 on OpenAlexaff
Mun Kim, Chunlei Zhang, C.-H. Lu, C.‐M. Hu

Bibliographic record

VenueApplied Physics Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhase noiseMicrowavePolaritonOscillator phase noiseNoise (video)PhysicsPhase (matter)OptoelectronicsMaterials scienceNoise figureOpticsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Low phase noise oscillators are key building blocks of many high-end microwave systems. This work introduces a phase noise reduction mechanism through a gain driven polariton platform, where coherent coupling is used to suppress frequency distribution around the carrier, effectively reducing the phase noise. The design process for achieving low phase noise performance is outlined, and three prototypes are constructed, all of which feature key components, such as gain-embedded planar microwave cavity, yttrium iron garnet, and magnets. In particular, the first prototype is used to showcase the phase noise reduction mechanism, while the second prototype, a fixed-frequency oscillator working at 3.544 GHz, exhibits phase noise levels of −117 and −132 dBc/Hz at 10 and 100 kHz offset frequencies, respectively. The third prototype offers a tuning range from 2.1 to 2.7 GHz, while maintaining phase noise levels comparable to the second prototype.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2024
Admission routes1
Has abstractyes

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