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Record W2884673121 · doi:10.1038/s41598-019-40497-7

Polarization- and wavelength-agnostic nanophotonic beam splitter

2019· preprint· en· W2884673121 on OpenAlexaff
David González‐Andrade, Christian Lafforgue, Elena Durán‐Valdeiglesias, Xavier Le Roux, Mathias Berciano, Éric Cassan, Delphine Marris‐Morini, Aitor V. Velasco, Pavel Cheben, Laurent Vivien, Carlos Alonso‐Ramos

Bibliographic record

VenueScientific Reports · 2019
Typepreprint
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersEuropean Metrology Programme for Innovation and ResearchHorizon 2020 Framework ProgrammeH2020 European Research CouncilMinisterio de Ciencia, Innovación y UniversidadesAgence Nationale de la RechercheEuropean CommissionComunidad de Madrid
KeywordsBeam splitterSplitterOpticsNanophotonicsSilicon on insulatorPhotonicsPower dividers and directional couplersFabricationOptoelectronicsPolarization (electrochemistry)Silicon photonicsOptical powerBandwidth (computing)WaveguideMaterials sciencePhotonic integrated circuitPhysicsSiliconTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

High-performance optical beam splitters are of fundamental importance for the development of advanced silicon photonics integrated circuits. However, due to the high refractive index contrast of silicon-on-insulator platforms, state-of-the-art nanophotonic splitters are hampered by trade-offs in bandwidth, polarization dependence and sensitivity to fabrication errors. Here, we present a new strategy that exploits modal engineering in slotted waveguides to overcome these limitations, enabling ultra-broadband polarization-insensitive optical power splitters with relaxed fabrication tolerances. The proposed splitter design relies on a single-mode slot waveguide that is gradually transformed into two strip waveguides by a symmetric taper, yielding equal power splitting. Based on this concept, we experimentally demonstrate -3 ± 0.5 dB polarization-independent transmission for an unprecedented 390 nm bandwidth (1260-1650 nm), even in the presence of waveguide width deviations as large as ±25 nm.

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 score0.003

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)0.0010.000

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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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