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Record W4410770912 · doi:10.1109/jstqe.2025.3574059

Single-Etch Silicon Nitride Grating Couplers for Multiband Applications in Quantum and Fiber Communications

2025· article· en· W4410770912 on OpenAlexafffund
Radovan Korček, Daniel Benedikovič, Cameron Horvath, Shurui Wang, Martin Vachon, Rubin Ma, Jens H. Schmid, Pavel Cheben, Winnie N. Ye

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsApplied Nanotools (Canada)National Research Council CanadaCarleton University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsSilicon nitrideGratingOptical fiberSiliconFiber Bragg gratingNitrideOpticsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Silicon nitride (Si3N4) is an attractive alternative to the silicon-on-insulator platform due to its broad spectral transparency window, low waveguide losses, and negligible twophoton absorption. However, the moderate refractive index contrast between the Si3N4 waveguide core and the cladding presents challenges, including limiting the efficiency and performance of surface grating fiber-chip coupling devices. Addressing this issue is crucial to fully leveraging the advantages offered by the silicon nitride platform. While various strategies have been developed to enhance the performance of surface grating couplers, they often come with increasingly complex fabrication requirements. In this work, we present a set of highefficiency silicon nitride grating couplers using standard singleetch fabrication processes. The devices are designed for three spectral regions: 950 nm, 1310 nm, and 1550 nm. Uniform grating couplers demonstrated experimental coupling efficiencies between -5.9 dB and -3.1 dB. Record-breaking performance was achieved using subwavelength metamaterial apodization and beam focalization, resulting in fiber-chip coupling losses as low as -2.5 dB, all achieved through a straight-forward single-etch fabrication process.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations3
Published2025
Admission routes2
Has abstractyes

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