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Record W2008069379 · doi:10.1364/oe.21.019624

Nanowires and sidewall Bragg gratings in silicon as enabling technologies for microwave photonic filters

2013· article· en· W2008069379 on OpenAlexafffund
Lawrence R. Chen, Jia Li, Mina Spasojevic, Rhys Adams

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsMcGill University
FundersCMC MicrosystemsNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaFonds de recherche du Québec – Nature et technologiesUniversity of WashingtonNational Science Foundation
KeywordsFiber Bragg gratingMaterials scienceOpticsSilicon photonicsSiliconOptoelectronicsGratingPhotonicsMicrowaveOptical filterWaveguidePhysicsWavelengthTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We describe the use of various silicon photonic device technologies to implement microwave photonic filters (MPFs). We demonstrate four-wave mixing in a silicon nanowire waveguide (SNW) to increase the number of taps for MPFs based on finite impulse response filter designs. Using a 12 mm long SNW reduces the footprint by five orders of magnitude compared to silica highly nonlinear fiber while only requiring approximately two times more input power. We also demonstrate optical delays based on serial sidewall Bragg grating arrays and step-chirped sidewall Bragg gratings in silicon waveguides. We obtain up to 63 ps delay in discrete steps from 15 ps to 32 ps over a wide bandwidth range from 33 nm to at least 62 nm. These components can be integrated with other silicon-based components such as integrated spectral shapers and modulators to realize a fully integrated MPF.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.234
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2013
Admission routes2
Has abstractyes

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