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Record W2010526503 · doi:10.1364/oe.18.007625

Subpicosecond optical pulse compression via an integrated nonlinear chirper

2010· article· en· W2010526503 on OpenAlexaff
Marco Peccianti, Marcello Ferrera, Luca Razzari, Roberto Morandotti, Brent E. Little, David Moss

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsOpticsPulse compressionBandwidth (computing)Electronic engineeringComputer sciencePicosecondOptoelectronicsMaterials scienceTelecommunicationsPhysicsEngineeringLaser

Abstract

fetched live from OpenAlex

Photonic integrated circuits (PICs) capable of ultra-fast, signal processing are recognized as being fundamental for future applications involving ultra-short optical pulse propagation, including the ability to meet the exponentially growing global fiber-optic telecommunications bandwidth demand. Integrated all-optical signal processors would carry substantial benefits in terms of performance, cost, footprint, and energy efficiency. Here, we demonstrate an optical pulse compressor based on an integrated nonlinear chirper, capable of operating on a sub-picosecond (> 1Tb/s) time scale. It is CMOS compatible and based on a 45cm long, high index doped silica glass waveguide we achieve pulse compression at relatively low input peak powers, due to the high nonlinearity and low linear and nonlinear losses of the device. The flexibility of this platform in terms of nonlinearity and dispersion allows the implementation of several compression schemes.

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 score0.002

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 head0.009
GPT teacher head0.234
Teacher spread0.226 · 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

Citations459
Published2010
Admission routes1
Has abstractyes

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