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Record W1968758327 · doi:10.1088/2040-8978/16/6/065007

Semi-analytical design methodology for large scale metal–insulator–metal waveguide networks

2014· article· en· W1968758327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Optics · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite-difference time-domain methodWaveguideMetal-insulator-metalPlasmonRange (aeronautics)Computer scienceInsulator (electricity)OpticsElectrical impedanceMaterials scienceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

A semi-analytical approach for efficient modelling of large scale networks of plasmonic metal–insulator–metal waveguides is proposed and its efficacy is assessed. A simple model for a wide range of waveguide junction configurations that can be obtained using waveguide impedance is utilized. This efficient and accurate model enables full analysis of a complete network of plasmonic waveguides without the need for any full-wave analysis. The proposed approach is computationally efficient and enables rapid design and optimization cycles using these networks. The results obtained using our approach match those obtained with finite difference time domain simulations. Several example structures have been analyzed using this approach, where their performance has been optimized through the multivariable optimization afforded by the technique reported here.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.394
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.076
GPT teacher head0.320
Teacher spread0.244 · 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