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Record W3026684942 · doi:10.1109/jlt.2020.2996424

Pulse Timing Jitter Estimated From Optical Phase Noise in Mode-Locked Semiconductor Quantum Dash Lasers

2020· article· en· W3026684942 on OpenAlexafffund
Youxin Mao, Zhenguo Lü, Jiaren Liu, Philip J. Poole, Guocheng Liu

Bibliographic record

VenueJournal of Lightwave Technology · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsJitterOpticsPhase noiseTerabitLaserSemiconductor laser theoryOptoelectronicsMode-lockingMaterials sciencePhysicsWavelength-division multiplexingTelecommunicationsComputer scienceWavelength

Abstract

fetched live from OpenAlex

The determination of timing jitter obtained from optical phase noise measurements is investigated in InAs/InP quantum dash Fabry-Pérot mode-locked coherent comb lasers with different pulse repetition rates. The results are compared with those determined through a direct measurement of the first harmonic of the RF power spectrum. Very good agreement is achieved. The ability to measure the timing jitter from optical phase noise is not restricted by the repetition rate of the laser being studied, allowing it to be applied to extremely high repetition rate lasers. Using these lasers we demonstrate 5.4 Tbit/s (PAM-4 48 × 28 GBaud PDM) and 10.3 Tbit/s (16 QAM 56 × 23 GBaud PDM) aggregate data transmission capacity. The ultra-low timing jitter exhibited by these devices make them excellent sources for multi-terabit optical networks.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.037
GPT teacher head0.316
Teacher spread0.279 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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