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Record W3185188341 · doi:10.1364/ome.434233

Universal micro-trench resonators for monolithic integration with silicon waveguides

2021· article· en· W3185188341 on OpenAlexafffund
Philippe Jean, Alexandre Douaud, Souleymane Toubou Bah, Sophie LaRochelle, Younès Messaddeq, Wei Shi

Bibliographic record

VenueOptical Materials Express · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsTrenchMaterials scienceResonatorSilicon on insulatorSiliconOptoelectronicsEtching (microfabrication)WaveguideChalcogenideSilicon photonicsOpticsNanotechnology

Abstract

fetched live from OpenAlex

We present a systematic study of micro-trench resonators for heterogeneous integration with silicon waveguides. We experimentally and numerically demonstrate that the approach is compatible with a large variety of thin film materials and that it does not require specific etching recipe development, thus making it virtually universal. The microresonators are fabricated through in-foundry silicon-on-insulator processing and in-house backend processing. We also report ultra-compact chalcogenide microresonators with radius as small as 5µ and quality factors up to 1.8 × 10 5 . We finally show a proof-of-concept of a novel multilayer waveguide using the micro-trench technique.

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 score0.034
Threshold uncertainty score0.553

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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