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Record W2108398426 · doi:10.1364/oe.15.006439

Design optimization of flattop interleaver and its dispersion compensation

2007· article· en· W2108398426 on OpenAlexafffund
John W. Y. Lit

Bibliographic record

VenueOptics Express · 2007
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsDispersion (optics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

The objective of this paper is to present a general strategy for design optimization of flattop interleavers, and dispersion compensation for the interleavers, in order to achieve superior optical performance. The interleaver is formed by two multi-cavity Gire-Tournois etalons (MC-GTE) in a Michelson Interferometer (MI). An interleaver that has m cavities in one etalon and n cavities in the other is called an mn-GTE interleaver. Our optimization strategy exploits the general flattop condition and the technique of ripple equalization. Any mn-GTE interleaver may be optimized. The spectral performance can be greatly improved by the optimization process. As an illustration, we present a comprehensive analysis for a 11-GTE and a 21-GTE interleaver. The analytical expressions for flattop conditions, peak and trough positions are derived for optimization. The optimal performance of the interleavers can be controlled by the reflection coefficients and the parameters m and n. To achieve low-dispersion mn-GTE flattop interleavers, we propose to use one additional MC-GTE as a dispersion compensator to compensate for the chromatic dispersion. The analytical expressions of group delays and chromatic dispersions for an MC-GTE interleaver are derived. The optimization strategy of dispersion-ripple equalization is explained. The results show that the dispersion performance can be tailored by changing the reflection coefficients of the MC-GTE, and the dispersion and bandwidth can be enhanced by increasing the number of cavities of the MC-GTE.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.264

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations29
Published2007
Admission routes2
Has abstractyes

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