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Record W3186103575 · doi:10.1088/1555-6611/ac1073

Modeling of a quantum dot gain chip in an external cavity laser configuration

2021· article· en· W3186103575 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLaser Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsExfo Electro-Optical Engineering (Canada)
Fundersnot available
KeywordsQuantum dotChipQuantum dot laserLaserPhotonicsOptoelectronicsPhysicsOpticsMaterials scienceSemiconductor laser theoryComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Abstract External cavity semiconductor lasers with strong optical feedback already exist using a gain chip medium. Owing to their ultrafast carrier dynamics, strong output power, and high temperature reliability, quantum dots as a gain medium are now envisioned as a promising solution to replace the current quantum well technology. This paper presents a semi-analytical rate equation model which is used to describe a quantum dot gain chip capable of lasing only with a free space external cavity laser. It investigates the evolution of the dynamical properties such as the turn-on delay and the damping rate. It also confirms the model’s validity through the relative intensity noise and the frequency noise with respect to both material and device parameters like the linewidth enhancement factor, the gain compression factor, or the cavity length. Overall, this numerical investigation provides initial building blocks for future fabrication research and development of high performance devices including filters or gratings as wavelength-selective components.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.462

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 head0.025
GPT teacher head0.248
Teacher spread0.222 · 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