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Record W2540485708 · doi:10.1364/ol.41.005059

High-efficiency apodized-imaging chip-fiber grating coupler for silicon nitride waveguides

2016· article· en· W2540485708 on OpenAlexaff
Yang Chen, Robert Halir, Íñigo Molina‐Fernández, Pavel Cheben, Jian‐Jun He

Bibliographic record

VenueOptics Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsNational Research Council Canada
FundersMinisterio de Economía y CompetitividadNational Natural Science Foundation of China
KeywordsOpticsGratingMaterials scienceBlazed gratingApodizationWaveguideFiber Bragg gratingOptoelectronicsSilicon nitrideWavelengthDiffraction gratingSiliconPhysics

Abstract

fetched live from OpenAlex

Chip-fiber grating couplers have been widely used in silicon-on-insulator platforms. In silicon nitride waveguides, the lower index contrast yields gratings with a weaker radiation strength and poor directionality, thereby limiting the coupling efficiency. Here we propose and design an apodized self-imaging grating coupler in silicon nitride that images an in-plane waveguide input to an output optical fiber placed at a specific distance above the chip. Both amplitude and phase apodizations are employed to engineer the transfer function of the self-imaging grating to produce an image field matching the fiber mode profile. Two-step etch staircase grating teeth are used to achieve directionality as high as 93%. Full three-dimensional finite-difference time-domain simulations show coupling from a 40 μm×40 μm grating to an SMF-28 single mode fiber with a record calculated efficiency of 86% (0.66 dB loss) and a 3 dB bandwidth of 40 nm near the 1550 nm wavelength.

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 score0.005

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 science0.0010.000
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 head0.234
Teacher spread0.225 · 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

Citations52
Published2016
Admission routes1
Has abstractyes

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