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Record W3012426173 · doi:10.1049/iet-opt.2019.0077

Integration of periodic, sub‐wavelength structures in silicon‐on‐insulator photonic device design

2020· article· en· W3012426173 on OpenAlexafffund
Yannick D’Mello, Orad Reshef, Santiago Bernal, Eslam El‐Fiky, Yun Wang, Maxime Jacques, David V. Plant

Bibliographic record

VenueIET Optoelectronics · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of OttawaMcGill University
FundersCMC MicrosystemsNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanophotonicsSilicon on insulatorPhotonicsOptoelectronicsWavelengthSilicon photonicsMaterials scienceBroadbandLithographyOpticsSiliconIntegration platformDispersion (optics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Rapid advances in high‐resolution chip lithography have accelerated nanophotonic device development on the silicon‐on‐insulator (SOI) platform. The ability to create sub‐wavelength features in silicon has attracted research in photonic band and dispersion engineering and consequently made available a wide array of device functionalities. By drawing on recent demonstrations, the authors review how periodic, sub‐wavelength structures are used for passive wave manipulation in SOI device design. The optical response is evaluated for both orthogonal polarisations at the telecom wavelengths of 1310 and 1550 nm. The results offer a versatile toolkit for the integration of these features in conventional nanophotonic device geometries. Notable benefits include a fine control of dispersion, wavelength and polarisation selectivity, and broadband performance.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.000
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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