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Record W2333248095 · doi:10.1109/jstqe.2016.2545634

The Importance of Single-Mode Behavior in Silicon-On-Insulator Rib Waveguides With Very Large Cross Section for Resonant Sensing Applications

2016· article· en· W2333248095 on OpenAlexafffund
Antoine Leblanc-Hotte, Jean‐Sébastien Delisle, Sylvie Lesage, Yves-Alain Peter

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsResonatorMaterials scienceMulti-mode optical fiberSingle-mode optical fiberOpticsModalSilicon on insulatorOptoelectronicsWaveguideCross section (physics)SiliconOptical fiberPhysicsComposite material

Abstract

fetched live from OpenAlex

Control of light properties through propagation in large cross section optical rib waveguides is of utmost importance to obtain the desired behavior, especially with resonant cavities. We have simulated, fabricated, and experimentally tested optical rib waveguides to evaluate the advantages of single-mode properties against multimode. Modal analysis of very large cross section rib waveguides showed that only particular dimensions allow a single-mode behavior. Furthermore, on-chip Fabry-Perot cavities were coupled to rib waveguides to assess impacts for a typical resonator. Experimental results are in good agreement with simulation guidelines, effectively highlighting the importance of single-mode behavior for resonant sensing applications.

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: 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
Metaresearch0.0010.001
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 head0.266
Teacher spread0.255 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

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