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Record W2936677492 · doi:10.1364/ao.58.002994

Grating couplers fabricated by e-beam lithography for long-range surface plasmon waveguides embedded in a fluoropolymer

2019· article· en· W2936677492 on OpenAlexaff
Zohreh Hirboodvash, Maryam Khodami, Norman R. Fong, Ewa Lisicka-Skrzek, Anthony Olivieri, Howard Northfield, R. Niall Tait, Pierre Berini

Bibliographic record

VenueApplied Optics · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCarleton UniversityNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsOpticsGratingMaterials scienceSurface plasmonSurface plasmon polaritonCoupling lossLithographyOptoelectronicsDiffraction gratingBlazed gratingOptical fiberCoupling (piping)PlasmonPhysics

Abstract

fetched live from OpenAlex

Long-range surface plasmon polariton waveguides consisting of Au stripes integrated with input and output grating couplers embedded in thick Cytop claddings are proposed and demonstrated experimentally. Under the right conditions, grating couplers enable broadside (top) coupling with good efficiency while producing a low level of background light. The scheme does not require high-quality input and output edge facets, and it simplifies optical alignments. We demonstrate coupling using a cleaved bow-tie fiber and a lensed fiber, and we determine the grating coupling efficiencies in both cases over a broad operating wavelength range. The lensed fiber produces a better overlap with the long-range surface plasmon mode of interest and thus results in a better coupling efficiency with essentially no background light as observed on an infrared camera. The measurements are compared with theoretical results obtained using a realistic model of the structures, including out-of-plane curvature in the grating profile resulting from our fabrication process. The coupling scheme along with the surface plasmon waveguides hold strong potential for biosensing applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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