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Record W2163115331 · doi:10.1109/ted.2003.809430

Experimental investigations of the effect of the mode-hopping on the noise properties of InGaAsP fabry-perot multiple-quantum-well laser diodes

2003· article· en· W2163115331 on OpenAlexaff
Vilius Palenskis, Jonas Matukas, Sandra Pralgauskaitė, J.G. Simmons, S. Smetona, R. Sobiestianskas

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster UniversityNortel (Canada)
Fundersnot available
KeywordsMaterials scienceActive layerOptoelectronicsDiodeNoise (video)LaserSemiconductor laser theoryOpticsPhysicsLayer (electronics)

Abstract

fetched live from OpenAlex

Detail studies of the optical and electrical low-frequency noise spectra and their correlation factor of graded-index separate-confinement-heterostructure, multiple-quantum-well (MQW) strained-layer Fabry-Perot (FP) InGaAsP/InP laser diodes have been carried out. It is shown that at defined DC currents and temperatures the intensive Lorentzian-type optical and electrical noise peaks (mode-hopping effect) of the FP lasers may be caused by different effects, since the correlation factor between the noises may be positive, negative, or close to zero, i.e., the diode terminal voltage (or resistance) and light output power can fluctuate in phase, in opposite phases or independently. It is determined that the shift of noise peak due to variation of temperature has an activated character. The activation energy is equal to nearly half the bandgap energy of the barrier layer or region, adjacent to the active region of the laser. It was also shown that coating the FP laser diode mirrors by a thin dielectric layer causes a substantial suppression of the mode-hopping effects, with only a small change of the light output power: i.e., the noise peaks are strongly related to the charge carrier and photon confinement in the active region and neighboring to the active regions.

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 score0.027
Threshold uncertainty score0.464

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 head0.236
Teacher spread0.222 · 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

Citations11
Published2003
Admission routes1
Has abstractyes

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