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Record W2065194460 · doi:10.1116/1.2186662

Gain spectra of 1.3μm GaInNAs laser diodes

2006· article· en· W2065194460 on OpenAlexaff
X. Zhang, J. A. Gupta, Pedro Barrios, G. Pakulski, X. Wu, A. Delâge

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNational Research Council CanadaInstitute for Microstructural SciencesUniversity of Ottawa
Fundersnot available
KeywordsBlueshiftLasing thresholdMaterials scienceSpectral lineLaserDiodeWavelengthQuantum well laserOpticsOptoelectronicsLaser diodeSemiconductor laser theoryPhotonPhysicsQuantum dot laserPhotoluminescence

Abstract

fetched live from OpenAlex

We present an experimental investigation of the optical gain properties of 1.3μm GaInNAs double quantum well ridge waveguide laser diodes. High-resolution gain spectra versus injection current and temperature were obtained by measuring the modulation depth introduced into the spontaneous emission spectrum by the Fabry-Pérot resonances. As the injection current increases, the modal gain spectral peak experiences a small blueshift over the photon energy, and the magnitude increases asymptotically, saturating at the lasing threshold level of 24.4cm−1. The peak of the modal gain spectra exhibits a redshift with an average rate of 0.58nm∕°C as the temperature increases from 30to50°C. For wavelengths corresponding to photon energy below the band gap, the modal gain spectra converge to the internal loss of 7cm−1. The full width at half maximum of the gain spectrum is 41.1meV at 30°C, 40mA and increases with injection current at a rate of 0.42meV∕mA. The high optical gain and low internal loss indicate that GaInNAs is a promising active material for long wavelength laser diodes.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.241
Teacher spread0.235 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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