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Polarizing Beamsplitter Grating based on Asymmetric Slot Waveguide Scatterer

2019· article· en· W2987741482 on OpenAlexaff
Ashutosh Patri, Stéphane Kéna‐Cohen, Christophe Caloz

Bibliographic record

Venue2019 Thirteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials) · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsOpticsGratingPolarization (electrochemistry)Beam splitterDiffraction gratingWaveguideGuided-mode resonanceReflection (computer programming)Materials scienceDiffraction efficiencyScatteringDiffractionTransmission (telecommunications)OptoelectronicsPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We demonstrate a novel design for grating-based polarizing beamsplitters (PBSs). The structure has a subwavelength thickness and is engineered to suppress all diffraction orders except for those of the desired orthogonal polarizations. This is achieved by using an asymmetric slot waveguide scatterer unit cell to ensure polarization-selective directional scattering. We first present the design rationale and demonstrate a PBS operating at ~ A = 752nm that reaches an efficiency of ~ 80% for both polarizations with a separation angle of ~ 80° between the polarized light beams. The structure has several advantages over typical grating or metasurface-based implementations, such as high efficiency at large separation angles, operation in any reflection-transmission mode (reflection only, transmission only, or both) and single periodic element requirement.

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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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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