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Record W2602689206 · doi:10.1063/1.4978945

Wavelength conversion of QAM signals in a low loss CMOS compatible spiral waveguide

2017· article· en· W2602689206 on OpenAlexafffund
Francesco Da Ros, Edson Porto da Silva, Darko Zibar, Brent E. Little, Roberto Morandotti, Michael Galili, David Moss, Leif Katsuo Oxenløwe

Bibliographic record

VenueAPL Photonics · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Research Council CanadaChinese Academy of SciencesDanmarks GrundforskningsfondAustralian Research CouncilNational Research Foundation
KeywordsQAMWavelengthSpiral (railway)CMOSOpticsWaveguideQuadrature amplitude modulationOptoelectronicsMaterials sciencePhysicsTelecommunicationsComputer scienceEngineering

Abstract

fetched live from OpenAlex

We demonstrate wavelength conversion of quadrature amplitude modulation (QAM) signals, including 32-GBd quadrature phase-shift keying and 10-GBd 16-QAM, in a 50-cm long high index doped glass spiral waveguide. The quality of the generated idlers for up to 20 nm of wavelength shift is sufficient to achieve a BER performance below the hard decision forward error correction threshold BER performance (<3.8 × 103), with an optical signal-to-noise ratio penalty of less than 0.3 dB compared to the original signal. Our results confirm that this is a promising platform for nonlinear optical signal processing, as a result of both very low linear propagation loss (<0.07 dB/cm) and a large material bandgap, which in turn ensures negligible nonlinear loss at telecom wavelengths.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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

Citations45
Published2017
Admission routes2
Has abstractyes

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