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Record W4229007813 · doi:10.1002/admt.202200038

Subwavelength Grating Metamaterial Multimode Bend for Silicon Waveguides

2022· article· en· W4229007813 on OpenAlexafffund
Kevan K. MacKay, Shurui Wang, Pavel Cheben, Winnie N. Ye

Bibliographic record

VenueAdvanced Materials Technologies · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsSilicon on insulatorExtinction ratioMaterials scienceMulti-mode optical fiberGratingWaveguideMetamaterialPhotonicsWavelengthOptoelectronicsRadius of curvaturePhysicsCurvatureSiliconOptical fiber

Abstract

fetched live from OpenAlex

Abstract In this paper, a novel multimode waveguide implemented on a silicon‐on‐insulator (SOI) platform that supports several transverse (TE)/transverse magnetic (TM) modes across a broad range of wavelengths is experimentally demonstrated. The fully etched metamaterial design combines a gradient curvature bend with trapezoidal subwavelength grating segments and tapered concentric bridging strips. The simulations showed successful propagation of up to nine modes (5 TE and 4 TM) on the 340 nm SOI platform, with excess losses below 2.4 dB, and intermodal cross‐talk of less than −15.7 dB. Experimentally, a compact multimode bend with a radius of 10 µm on a 220 nm SOI platform is demonstrated that successfully supports four TE modes, with average signal‐to‐cross‐talk extinction ratio levels of 14.4 dB or better, across a wavelength band of 1500–1600 nm. This versatile multimode waveguide bend can be employed as a fundamental building block for densely integrated photonic circuits and mode multiplexing systems.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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