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Record W1984819067 · doi:10.1117/12.699733

Implications of injection current and optical input power on the performance of reflective semiconductor optical amplifiers

2007· article· en· W1984819067 on OpenAlexfundno aff
Ning Cheng, L.G. Kazovsky

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsExtinction ratioOptical amplifierOptical powerOpticsPhysicsModulation (music)SIGNAL (programming language)Optical communicationAmplifierMaterials scienceOptoelectronicsComputer scienceLaser

Abstract

fetched live from OpenAlex

A time domain model for reflective semiconductor optical amplifiers (RSOAs) is developed based on the carrier rate equation and wave propagation equation. In this model, the gain saturation effect and the dependence of spontaneous carrier lifetime on carrier density are explicitly included, and the evolution of carrier density and the optical power in time and space under current modulation is considered in detail. Using the time domain model, the performance of RSOAs with different active layer lengths is investigated under different inject current densities and input optical powers. Numerical simulations reveal that the carrier spontaneous lifetime is the foremost limiting factor of RSOA modulation speed, but increasing photon density improves RSOA performance. With increased bias currents or optical input powers, the small signal frequency response is improved and the eye closure penalty under large signal on-off key modulation is reduced, but the extinction ratio of the optical output signal is decreased. Under the same bias current density and optical input power, RSOAs with longer active layers exhibit improved frequency response and smaller eye closure penalty.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.669

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 head0.015
GPT teacher head0.263
Teacher spread0.248 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSemiconductor Quantum Structures and DevicesFrench-language works237,207