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Record W4297547870 · doi:10.3390/photonics9100695

Ultralow Noise and Timing Jitter Semiconductor Quantum-Dot Passively Mode-Locked Laser for Terabit/s Optical Networks

2022· article· en· W4297547870 on OpenAlexaff
Youxin Mao, Guo‐Cheng Liu, Khan Zeb, Zhenguo Lü, Jiaren Liu, Philip J. Poole, Pedro Barrios

Bibliographic record

VenuePhotonics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptoelectronicsTerabitOpticsLaser linewidthJitterMaterials scienceOptical amplifierQuantum dot laserSemiconductor laser theoryLaserPhysicsWavelength-division multiplexingSemiconductorWavelengthTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Diode optical frequency comb lasers are promising compact solutions to generate high-speed optical pulses for applications in high spectral efficiency wavelength division multiplexing transmission with advanced modulation formats. In this paper, an InAs/InP quantum dot (QDot) C-band single-section passively mode-locked laser (MLL) based broadband optical frequency comb source with a free spectral range of 28.4 GHz is presented. The device exhibits less than 1.5 MHz optical linewidth (phase noise) over 56 channels and 2.1 fs pulse-to-pulse timing jitter with a central wavelength of 1550 nm. Using this comb, we demonstrate an aggregate data transmission capacity of 12.5 Terabit/s over 100 km of standard single mode fiber by employing dual-polarization with 16 QAM modulation format. This investigation shows the viability for semiconductor QDot MLLs to be used as low-cost optical source in Terabit/s or higher 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)0.000
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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