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Record W2101277131 · doi:10.1109/cleo.2001.948066

Stability properties of dispersive extended-cavity semiconductor lasers

2001· article· en· W2101277131 on OpenAlexaff
Lora Ramunno, J. E. Sipe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGratingOpticsSemiconductor laser theoryLaserMaterials scienceDiodeOptoelectronicsDiffraction gratingDistributed feedback laserReflection (computer programming)PhysicsComputer science

Abstract

fetched live from OpenAlex

Summary form only given. Since semiconductor diode dispersive extended-cavity lasers are currently of interest for a variety of applications, understanding their stability properties is important to ensure stable CW laser operation. These lasers consist of a semiconductor diode coupled to some sort of external dispersive reflector (e.g. a fiber grating) that forms one mirror of the laser cavity. Quite unexpectedly, experiments have shown that for chirped fiber grating lasers (FGL), the orientation of the grating drastically alters the stability of CW operation: when the grating was placed such that the index modulation period decreased with distance from the coupled diode facet, stable single mode operation occurred, but the opposite grating orientation resulted in significant mode-hopping. This result is counter-intuitive, since the grating reflectivity spectra are identical in both cases; the only difference is in the sign of the curvature of the reflection spectrum phases, and this has been shown to play only a small role in large-scale current modulation dynamics. In this presentation, we not only explain these curious experimental results, but we also find a simple numerical prescription with which the stability of any such system can be assessed, provided the reflection spectrum of the external reflector is known.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.954

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 head0.027
GPT teacher head0.204
Teacher spread0.177 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

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