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Record W2073632731 · doi:10.1109/jqe.2003.818287

Performance simulation and design optimization gain-clamped semiconductor optical amplifiers based on distributed Bragg reflectors

2003· article· en· W2073632731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOptical amplifierOpticsDistributed Bragg reflectorGain compressionLasing thresholdSemiconductor laser theoryMaterials scienceOptoelectronicsGratingAmplifierOptical powerNoise figureNoise (video)Fiber Bragg gratingWavelengthSemiconductorPhysicsLaserComputer science

Abstract

fetched live from OpenAlex

Performance of the gain-clamped semiconductor optical amplifier (GC-SOA) based on distributed Bragg reflector (DBR) structures is investigated by a comprehensive broad-band time-domain traveling wave model. Critical factors, e.g., the material gain profile, the waveguide grating structures, the longitudinal variation of the optical field, and the carrier density as well as the broad-band spontaneous noise emission are considered in the model. The effects of operation parameters (e.g., the electrical bias current and the input optical power) and design parameters (e.g., the normalized coupling coefficient /spl kappa/L and the lasing wavelength position /spl lambda//sub L/) on device performance are examined in detail. Based on the results of the performance simulation, guidelines for design optimization are discussed and, in particular, unbalanced grating configurations combined with nonuniform current injection schemes are proposed to reduce the noise figure without much sacrifice on other device performance.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.937

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 head0.028
GPT teacher head0.284
Teacher spread0.255 · 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