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Record W4282011990 · doi:10.1364/oe.462863

Broadband nonreciprocal spoof plasmonic phase shifter based on transverse Faraday effects

2022· article· en· W4282011990 on OpenAlexaff
Hao Yan, Liqiao Jing, Jia Zhao, Chuanning Niu, Yujie Zhang, Liuge Du, Zuojia Wang

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsMcMaster University
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsPhysicsOpticsMetamaterialSurface plasmon polaritonPlasmonTransverse planeOptoelectronicsPhase shift moduleTransverse modeMicrowaveSurface plasmonInsertion lossLaser

Abstract

fetched live from OpenAlex

Spoof surface plasmon polaritons (SSPPs) have aroused widespread concern due to their strong ability in field confinement at low frequencies. For miniaturized integrated circuits, there is a pressing need for nonreciprocal spoof plasmonic platforms that provide diode functionalities. In this letter, we report the realization of nonreciprocal phase shifting in SSPPs using the transverse Faraday effect. A plasmonic coupled line is constructed by flipped stacking two corrugated metallic strips, in order to enhance the mode coupling between evanescent waves that carry opposite transverse spin angular momenta. With a transverse magnetized ferrite cladding, the SSPP mode is split into two circularly-polarized ones that show different propagation constants over a broad band. A nonreciprocal phase shifter compatible to standard microstrips is designed to validate the breaking of time-reversal symmetry in SSPPs. Microwave measurement demonstrates a differential phase shift up to 46.2°/cm from 12 GHz to 15 GHz. Owing to the advantages of strong field confinement and contactless ferrite integration, the proposed method enables an alternative pathway for nonreciprocal spoof interconnects.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
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 integrity0.0000.001
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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