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Record W4391547629 · doi:10.1109/jqe.2024.3357031

Vertical Coupling Effect on Gain Bandwidth of Chirped InAs/InP Quantum Dot Structures

2024· article· en· W4391547629 on OpenAlexafffund
Gaowen Chen, Fujuan Huang, Xiupu Zhang

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum dotOptoelectronicsQuantum dot laserBandwidth (computing)BroadbandOpticsLaserMaterials scienceSemiconductor laser theoryQuantum wellPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Quantum dot (QD) devices are usually desired to have a broadband gain spectrum. An alternative solution to achieve a broadband gain in QD devices is to use multiple layers with different QD heights, which are stacked vertically, i.e. a chirped QD structure in the active region. In the chirped stacked QD structure, the vertical strain and electron coupling effect have a significant impact on the optical transition property and thus optical gain bandwidth. However, previous studies on the vertical coupling effect have mainly focused on uniformly stacked QD structures, and the chirped QD structures have not been investigated carefully. This work presents a detailed analysis of the vertical coupling effect in chirped QD structures (i.e. ascending and descending chirped structure) and its impact on the optical gain bandwidth of the active region. It is found that the descending chirped structure leads to a wider gain bandwidth, in particular at high current injection. A Fabry-Perot mode-locked laser with the descending chirped structure presents a better performance in pulse width and frequency comb lines compared to the ascending chirped structure.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.282
Teacher spread0.272 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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