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Record W2047761502 · doi:10.1109/jqe.2004.836026

Investigation of semiconductor optical amplifier integrated with DBR laser for high saturation power and fast gain dynamics

2004· article· en· W2047761502 on OpenAlexaff
Jongwoon Park, Wei‐Ping Huang, Xun Li

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2004
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLasing thresholdDistributed Bragg reflector laserSemiconductor laser theoryOptical amplifierLaserDistributed Bragg reflectorMaterials scienceOpticsOptoelectronicsGainSemiconductor optical gainLaser power scalingAmplifierGain-switchingActive laser mediumSemiconductorPhysics

Abstract

fetched live from OpenAlex

A detailed theoretical investigation of an integration of a semiconductor optical amplifier (SOA) and distributed Bragg reflector (DBR) laser is presented. The dependence of the device performance on those key design parameters such as the lasing wavelength, light injection direction (co- and contra-propagation), lasing power of the DBR laser, and the biasing condition of the SOA is examined systematically by means of a comprehensive time-domain traveling-wave model. As this integrated structure is particularly designed for high saturation power and fast gain dynamics, these characteristics are simulated and compared with the results from the conventional structures. Depending on different requirements, superior performance on either saturation power or noise figure without compromise on the optical gain can be achieved by different integration configurations (i.e., by different light injection directions). For the structure with the light injection from the output end of the SOA (namely, the integrated SOA-laser structure), the fast gain dynamics is found through simulation, which helps to reduce the large-signal waveform distortion in the amplification of narrow pulses.

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 score0.240
Threshold uncertainty score0.478

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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