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Record W2486227722 · doi:10.1364/ol.41.003443

Controlling leakage losses in subwavelength grating silicon metamaterial waveguides

2016· article· en· W2486227722 on OpenAlexaff
José Darío Sarmiento-Merenguel, Alejandro Ortega‐Moñux, Jean-Marc Fédéli, J. Gonzalo Wangüemert‐Pérez, Carlos Alonso‐Ramos, Elena Durán‐Valdeiglesias, Pavel Cheben, Íñigo Molina‐Fernández, Robert Halir

Bibliographic record

VenueOptics Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersMinisterio de Economía y Competitividad
KeywordsMaterials scienceGratingSilicon on insulatorLeakage (economics)MetamaterialOpticsDielectricWaveguideSiliconRefractive indexOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Subwavelength gratings (SWG) are photonic structures with a period small enough to suppress diffraction, thereby acting as artificial dielectric materials, also called all-dielectric metamaterials. This property has been exploited in many high-performance photonic integrated devices in the silicon-on-insulator (SOI) platform. While SWG waveguides are theoretically lossless, they may exhibit leakage penalty to the substrate due to a combination of reduced modal confinement and finite thickness of the buried oxide (BOX) layer. In this Letter, for the first time, to the best of our knowledge, we analyze substrate leakage losses in SWG waveguides. We establish a direct relation between the effective index of the waveguide mode and the leakage losses which, remarkably, is independent of the geometric parameters of the SWG waveguide. This universal relation is demonstrated both numerically and experimentally, and it provides practical design guidelines to mitigate leakage losses. For BOX thicknesses of 2 and 3 μm, we find negligible leakage losses when the mode effective index is higher than 1.65 and 1.55, respectively.

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 score0.273
Threshold uncertainty score0.515

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

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