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Record W2015298958 · doi:10.1364/oe.20.012473

Polarization-controlled excitation of multilevel plasmonic nano-circuits using single silicon nanowire

2012· article· en· W2015298958 on OpenAlexaff
Mohamed H. El Sherif, Osman Ahmed, Mohamed H. Bakr, Mohamed A. Swillam

Bibliographic record

VenueOptics Express · 2012
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPlasmonOpticsPolarization (electrochemistry)OptoelectronicsMaterials scienceNanowireElectronic circuitSurface plasmonBeam splitterNanophotonicsPhysicsLaser

Abstract

fetched live from OpenAlex

We propose a surface plasmon polarization-controlled beam splitter based on plasmonic slot waveguides (PSWs). It couples light of different polarizations from a silicon nanowire into multilevel plasmonic networks. Two orthogonal PSWs are utilized as the guiding waveguides for each polarization. The proposed structure overcomes inherent polarization limitation in plasmonic structures by providing multilevel optical signal processing. This ability of controlling polarization can be exploited to achieve 3-D multilevel plasmonic circuits and polarization controlled chip to chip channel. Our device is of a compact size and a wide band operation. The device utilizes both quasi-TE and quasi-TM polarizations to allow for increased optical processing capability. The crosstalk is minimal between the two polarizations propagating in two different levels. We achieve good transmission efficiency at a wavelength of 1.55 µm for different polarizations. We analyze and simulate the structure using the FDTD method. The proposed device can be utilized in integrated chips for optical signal processing and optical computations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.881

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 head0.033
GPT teacher head0.257
Teacher spread0.224 · 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 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

Citations24
Published2012
Admission routes1
Has abstractyes

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