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Record W4206746836 · doi:10.1109/jlt.2022.3142161

Mutlilayer Wedge Disks CROW for an Optical Delay Line

2022· article· en· W4206746836 on OpenAlexafffund
Marc-Antoine Bianki, Cédric Lemieux‐Leduc, Régis Guertin, Yves-Alain Peter

Bibliographic record

VenueJournal of Lightwave Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsResonatorOpticsWedge (geometry)Insertion lossOptical switchPhysicsBandwidth (computing)OptoelectronicsMaterials scienceTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Optical delay lines are vital for the development of all optical telecommunications, optical computers and for many nonlinear and quantum optics processes. Periodic structures such as coupled resonator optical waveguides (CROW) achieve high delays in a compact manner and with a large bandwidth. Wedge disk resonators have demonstrated high quality factors compared to rectangular disks and rings while remaining integrable on chip. Vertical coupling is needed since the optical mode is pushed toward the disk center by the wedge. We propose an optical delay line made of wedge disks arranged in two overlapping layers. Disks are made of silicon dioxide on silicon pillars. Numerical calculations of the device performance and an experimental proof of concept are presented. A maximum delay of 85 ps for eleven disks is achieved with a loss per cavity of 3.91 dB. This is a first proof of concept of a CROW structure made of wedge disk coupled vertically.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.448

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 integrity0.0000.001
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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