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Record W3080759745 · doi:10.1051/epjconf/202023801003

Quantum dot multi-wavelength lasers for Tbit/s coherent communications and 5G wireless networks -INVITED

2020· article· en· W3080759745 on OpenAlexaffabout
Zhenguo Lü, J.R. Liu, Youxin Mao, Khan Zeb, G.C. Liu, J. Webber, M. Rahim, G. Pakulski, Chunying Song, M. Vachon, Pedro Barrios, Daniel Poitras, S.R. Wang, Weihong Jiang, Philip J. Poole

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsLaser linewidthOptoelectronicsLaserOpticsTerabitRelative intensity noisePhase noiseMaterials scienceQuantum dotQuantum dot laserPhysicsWavelength-division multiplexingWavelengthSemiconductor laser theory

Abstract

fetched live from OpenAlex

We will present the design, growth, fabrication, electronic control and packaging of the InAs/InP quantum dot (QD) multi-wavelength lasers (MWLs) developed by AEP of NRC in Canada. Their key technical specifications include L-I-V curves, optical and RF beating spectra, relative intensity noise (RIN), and optical phase noise of each individual wavelength channel, as well as timing jitter are investigated. Data bandwidth transmission capacity of 5.376 Tbit/s and 10.3 Tbit/s respectively in the PAM-4 and 16-QAM modulation formats are demonstrated by only using a single QD MWL chip. We have also developed a novel monolithic InAs/InP QD dual-wavelength (DW) DFB laser as a compact optical beat source to generate millimeter-wave (MMW) signals. Because of using a common cavity, highly coherent and correlated optical modes with optical linewidth as low as 15.83 kHz, spectrally pure MMW signals around 46.8 GHz with a linewidth down to 26.1 kHz were experimentally demonstrated. By using this QD DW-DFB laser, a 1Gbaud (2Gbps) MMW over-fiber transmission link is demonstrated with PAM-4 signals. The results show that the demonstrated device is suitable for high speed high capacity MMW fiber-wireless integrated fronthaul of 5G 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

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 head0.060
GPT teacher head0.263
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2020
Admission routes2
Has abstractyes

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