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Record W1971700217 · doi:10.1109/cleo.2000.906882

Polarization-dependence in multiquantum well lasers and semiconductor optical amplifiers: probing interwell transport effects

2000· article· en· W1971700217 on OpenAlexaff
Dayan Ban, Edward C. Wong, Edward H. Sargent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum wellCharge-carrier densityLaserOptical amplifierSemiconductorSemiconductor laser theorySemiconductor optical gainOptoelectronicsMaterials sciencePolarization (electrochemistry)PhotonicsAmplifierOpticsPhysicsChemistry

Abstract

fetched live from OpenAlex

Summary form only given. The advantages and opportunities associated with the use of multiple quantum wells (MQWs) in semiconductor laser and semiconductor optical amplifier (SOA) active regions are accompanied by challenges. In a system with many wells separated by spatially thick or energetically high barriers, injected nonequilibrium carriers may become nonuniformly distributed among the wells. The subject of nonuniform carrier density distributions has attracted significant attention because it results in degradation in both static and dynamic performance: increased threshold current, above-threshold efficiency roll-off, lowered dynamic performance and worsened chirp. While the mechanisms underlying the problem of interwell carrier density nonuniformity have been clarified via numerical modeling, the phenomenon remains to be observed directly via experiment. We propose a method of using the difference in the dependence of TE vs. TM modal gain on nonuniformity of carrier density as a direct experimental probe of interwell transport effects. We illustrate the proposed mechanism: due to the difference in the spatial configurations of TE and TM optical modes, the TE and TM modes sample differently the effects of a nonuniform distribution of carriers among the wells.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.999

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)0.0020.000

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 head0.229
Teacher spread0.223 · 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.

Study designObservational
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
Published2000
Admission routes1
Has abstractyes

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