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Record W2556200330 · doi:10.1002/lpor.201600213

Ultra‐broadband nanophotonic beamsplitter using an anisotropic sub‐wavelength metamaterial

2016· article· en· W2556200330 on OpenAlexaff
Robert Halir, Pavel Cheben, José Manuel Luque‐González, Jose Darío Sarmiento‐Merenguel, Jens H. Schmid, J. Gonzalo Wangüemert‐Pérez, Dan‐Xia Xu, Shurui Wang, Alejandro Ortega‐Moñux, Íñigo Molina‐Fernández

Bibliographic record

VenueLaser & Photonics Review · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersSecretaría de Estado de Investigación, Desarrollo e InnovaciónMinisterio de Economía y CompetitividadUniversidad de Málaga
KeywordsNanophotonicsBroadbandPhotonicsMetamaterialOptoelectronicsOpticsBandwidth (computing)Beam splitterPhysicsComputer scienceTelecommunicationsLaser

Abstract

fetched live from OpenAlex

Abstract Nanophotonic beamsplitters are fundamental building blocks in integrated optics, with applications ranging from high speed telecom receivers to biological sensors and quantum splitters. While high‐performance multiport beamsplitters have been demonstrated in several material platforms using multimode interference couplers, their operation bandwidth remains fundamentally limited. Here, we leverage the inherent anisotropy and dispersion of a sub‐wavelength structured photonic metamaterial to demonstrate ultra‐broadband integrated beamsplitting. Our device, which is three times more compact than its conventional counterpart, can achieve high‐performance operation over an unprecedented 500 nm design bandwidth exceeding all optical communication bands combined, and making it one of the most broadband silicon photonics components reported to date. Our demonstration paves the way toward nanophotonic waveguide components with ultra‐broadband operation for next generation integrated photonic systems. image

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 score0.000
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)0.0000.000

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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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

Citations194
Published2016
Admission routes1
Has abstractyes

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