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Record W1940634887 · doi:10.1109/jstqe.2015.2479359

Performance of Planar, Rib, and Photonic Crystal Silicon Waveguides in Tailoring Group-Velocity Dispersion and Mode Loss

2015· article· en· W1940634887 on OpenAlexfundno aff
Rezwanul Haque Khandokar, Masuduzzaman Bakaul, Thas Nirmalathas, Md Asaduzzaman

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersInformation Technology Research CentreIndependent University, BangladeshNational ICT AustraliaYonsei UniversityUniversity of MelbourneMonash UniversityBangladesh University of Engineering and Technology
KeywordsPlanarGroup velocityPhotonic crystalMaterials scienceDispersion (optics)SiliconOpticsOptoelectronicsSlow lightSilicon photonicsMode (computer interface)PhysicsComputer science

Abstract

fetched live from OpenAlex

Nanophotonic technologies have attracted a lot of attention to co-develop optical and electronic devices on silicon (Si) that further miniaturize modern optical communication systems. Optical properties of these miniaturized devices are highly dependent on waveguide geometry and can be tailored for various applications with minor changes in cross-sectional areas. This paper investigates the performance of widely discussed planar, rib, and photonic crystal Si waveguides by manipulating the dimensions of Si core designed for single mode operation across the 1.2 to 1.6 μm telecommunication bands. Each of the waveguide is studied and compared for a set of properties, including effective refractive index, group index, group-velocity dispersion, and mode loss, with and without narrow bends, which can be instrumental in selecting the correct type and geometry of the waveguide required for any specific application.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.601

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)0.0000.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 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

Citations10
Published2015
Admission routes1
Has abstractyes

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