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Record W3195075748 · doi:10.3390/photonics8080340

Spectral Output of Homogeneously Broadened Semiconductor Lasers

2021· article· en· W3195075748 on OpenAlexaff
Daniel T. Cassidy

Bibliographic record

VenuePhotonics · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLaserSemiconductor laser theorySpontaneous emissionOptoelectronicsOpticsDiodeAmplified spontaneous emissionSemiconductor optical gainSemiconductorPhysicsGain-switchingMaterials science

Abstract

fetched live from OpenAlex

Gain, spontaneous emission, and reflectance play important roles in setting the spectral output of homogeneously broadened lasers, such as semiconductor diode lasers. This paper provides a restricted-in-scope review of the steady-state spectral properties of semiconductor diode lasers. Analytic but transcendental solutions for a simplified set of equations for propagation of modes through a homogeneously broadened gain section are used to create a Fabry–Pérot model of a diode laser. This homogeneously broadened Fabry–Pérot model is used to explain the spectral output of diode lasers without the need for guiding-enhanced capture of spontaneous emission, population beating, or non-linear interactions. It is shown that the amount of spontaneous emission and resonant enhancement of the reflectance-gain (RG) product as embodied in the presented model explains the observed spectral output. The resonant enhancement is caused by intentional and unintentional internal scattering and external feedback.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.207
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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