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Record W4392577742 · doi:10.1117/12.3003151

Ultra-broadband quantum dot coherent comb lasers

2024· article· en· W4392577742 on OpenAlexaff
Guocheng Liu, Zhenguo Lü, Philip J. Poole, Jiaren Liu, Martin Vachon, Xiaoran Xie, Qi Yang, John Weber, Youxin Mao, Pedro Barrios, P. Waldron, Mohamed Rahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBroadbandQuantum dot laserOptoelectronicsQuantum dotLaserFrequency combSemiconductor laser theoryMaterials sciencePhysicsOptics

Abstract

fetched live from OpenAlex

In this work, we have investigated self-injection locking effects on a full spectral system with selective single-channel injection and full-channel injection in a quantum dot mode-locked comb laser through an optical feedback loop. It has been noticed that self-injection locking can not only improve the performance of a single-channel laser system but also has a strong effect on the whole spectral behavior. In the case of single-channel self-injection, we investigated the effects under a locked regime above the injection-locking threshold P<sub>SIL</sub>. The locked lines were strongly enhanced with intensities high above the broad spectrum and also intensified even outside of the original spectral bandwidth. The typical feature is a big dip (or hole) appearing on the high-energy side of the lines if it is within the free-running spectral region. We have investigated this asymmetric phenomenon. It is considered that the locked modes are highly intensified at the expense of higher energy carriers excited by currents. The locking process transferred the energy from the lasing mode to the locked mode. For the full channel self-injection, the system was set under a controllable self-injection locking condition. A bandwidth enhancement phenomenon can be observed when injected power reaches the self-injection locking threshold PSIL, and the broadening gets stronger till to the locked regime. Finally, the original spectral bandwidth had been significantly broadened. This bandwidth broadening goes to both sides of the free-running spectrum and the broadening is remarkable.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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