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Record W2089138745 · doi:10.1364/opex.13.005472

1-D slab photonic crystal k-vector superprism demultiplexer: analysis, and design

2005· article· en· W2089138745 on OpenAlexaff
Aref Bakhtazad, Andrew G. Kirk

Bibliographic record

VenueOptics Express · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsOpticsPhotonic crystalPrismDemultiplexerMaterials scienceCollimated lightPlane wave expansionDispersion (optics)WavelengthPhysicsMultiplexingTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

The design of a complete demultiplexer based on the k-vector superprism in a 1-D slab photonic crystal is proposed. This design scales to resolve 32 channels spaced by 0.8 nm (100 GHz) in the C band for a dense wavelength division multiplexing system. It is shown that a prism area of 0.017 mm;2 is sufficient for the required wavelength resolution using typical silicon-on-insulator technology and that the total chip size would be 4x3 mm;2. In order to achieve this, the modest angular dispersion of a 1-D slab photonic crystal is enhanced by considerably expanding the input beam through the superprism region and employing etched mirrors to collimate and focus the light into and out of the superprism. The plane wave expansion method is used to obtain the wave vector diagram and from this we develop design equations based on conventional ray tracing. We then present an optimization approach which minimizes the prism area whilst maintaining the necessary dispersion. Finally the non-uniformity of phase velocity dispersion across the desired spectral window is addressed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.712

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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