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Record W2056599267 · doi:10.1364/ol.37.003807

From classical four-wave mixing to parametric fluorescence in silicon microring resonators

2012· article· en· W2056599267 on OpenAlexaff
Stefano Azzini, Davide Grassani, Mattéo Galli, Lucio Claudio Andreani, Marc Sorel, Michael J. Strain, L. G. Helt, J. E. Sipe, Marco Liscidini, Daniele Bajoni

Bibliographic record

VenueOptics Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFour-wave mixingResonatorParametric statisticsMixing (physics)Nonlinear systemPhysicsNonlinear opticsPhotonicsQuantumQuantum opticsOpticsQuantum mechanicsStatistical physicsOptoelectronicsMathematics

Abstract

fetched live from OpenAlex

Four-wave mixing (FWM) can be either stimulated or occur spontaneously. The first process is intrinsically much stronger and well understood through classical nonlinear optics. The latter, also known as parametric fluorescence, can be explained only in the framework of a quantum theory of light. We experimentally demonstrated that, in a microring resonator, there is a simple relation between the efficiencies of these two processes that is independent of the nonlinearity and ring size. In particular, we have shown the average power generated by parametric fluorescence can be immediately estimated from a classical FWM experiment. These results suggest that classical nonlinear characterization of a photonic integrated structure can provide accurate information on its nonlinear quantum properties.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.022
GPT teacher head0.223
Teacher spread0.201 · 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

Citations87
Published2012
Admission routes1
Has abstractyes

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