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Record W2133565473 · doi:10.1109/jlt.2012.2188884

Ring Resonator Optical Gyroscopes—Parameter Optimization and Robustness Analysis

2012· article· en· W2133565473 on OpenAlexaff
Miguel Ángel Guillén-Torres, Edmond Cretu, Nicolas A. F. Jaeger, Lukas Chrostowski

Bibliographic record

VenueJournal of Lightwave Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGyroscopeResonatorSagnac effectExtinction ratioOpticsRobustness (evolution)Drop (telecommunication)Ring laser gyroscopeElectronic engineeringPhysicsMaterials scienceEngineeringTelecommunicationsWavelength

Abstract

fetched live from OpenAlex

Semiconductor ring resonators as the core components of instruments and devices have found applications in the areas of telecommunications and sensors. In this study, the feasibility of using ring resonators as solid-state angular rate sensors (gyroscopes) based on the Sagnac effect is assessed, and an extensive analysis of the optimal values of resonator length, coupling, and detuning is carried out, for the drop port of a double-bus ring, and for an all-pass, single-bus ring, for different values of propagation losses, consistent with different available technologies. We show that for both the all-pass and the drop-port configurations, optimally undercoupled rings show larger extinction ratios and, thus, better resolutions than critically coupled rings of the same length, contrary to our initial intuitive assumption that critically coupled rings should offer the best resolutions. Our analysis also shows that the ring resonator gyroscopes require a technology-constrained optimization, with the propagation loss as the main factor that hinders the resolution, and that determines the optimum values of all other parameters, namely length, coupling, and resonance detuning. According to our model, standard-chip-sized racetrack gyroscopes are suitable for rate- and tactical-grade applications for selected, currently feasible low propagation loss waveguides.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations47
Published2012
Admission routes1
Has abstractyes

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