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Record W4312532943 · doi:10.1109/jlt.2022.3220982

Influence of Losses, Device Size, and Mode Confinement on Integrated Micro-Ring Resonator Performance for Absorption Spectroscopy Using Evanescent Field Sensing

2022· article· en· W4312532943 on OpenAlexafffund
Pauline Girault, Laurent Oyhénart, Théo Rouanet, S. Joly, Guillaume Beaudin, Michael Canva, Laurent Béchou, Paul G. Charette

Bibliographic record

VenueJournal of Lightwave Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de Sherbrooke
FundersÉcole Centrale de LyonInstitut National des Sciences Appliquées de LyonCentre National de la Recherche ScientifiqueFonds de recherche du Québec – Nature et technologiesUniversité Grenoble AlpesAgence Nationale de la RechercheNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeIndian National Science Academy
KeywordsResonatorSpectroscopyOpticsMaterials scienceRefractive indexCavity ring-down spectroscopyAbsorption (acoustics)Coupling (piping)Sensitivity (control systems)Split-ring resonatorOptoelectronicsAbsorption spectroscopyPhysicsElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

This work presents a detailed modeling-based analysis of integrated micro-ring resonators used for absorption spectroscopy. Generally, sensors based on micro-ring resonators detect changes in the real part of the sensing medium refractive index, at critical coupling. In absorption spectroscopy, however, micro-ring resonators are used to measure changes in the imaginary part of the index and are most sensitive away from critical coupling, with separate maxima in the under- and over-coupled regimes. In this work, we present a detailed analysis of the under-coupled regime, explaining the relationships between sensitivity, mode confinement, and losses. The analysis is based on reverse-symmetry waveguides to increase the proportion of mode power in the sensing medium and incorporates a realistic model of propagation losses based on experimental measurements of sidewall roughness. The analysis demonstrates that the resonant nature of the sensor is most effective at small radii compared to a non-resonant structure of equal size and shows a behavior of diminishing returns at larger device sizes regarding sensitivity and elevated proportions of mode power in the evanescent field.

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 score0.088
Threshold uncertainty score0.424

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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