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Record W2055328338 · doi:10.1109/iciprm.2010.5516172

Temperature sensitivity of 1.55μm (100) InAs/InP-based quantum dot lasers

2010· article· en· W2055328338 on OpenAlexaff
Sayid A. Sayid, Igor P. Marko, Stephen J. Sweeney, Philip J. Poole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsInstitute for Microstructural Sciences
FundersEngineering and Physical Sciences Research Council
KeywordsQuantum dotQuantum dot laserSpontaneous emissionOptoelectronicsLaserSemiconductor laser theoryMaterials scienceQuantum wellCurrent densityCurrent (fluid)SemiconductorPhysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Semiconductor lasers with quantum dot (QD) based active regions have generated a huge amount of interest for applications including communications networks due to their anticipated superior physical properties due to three dimensional carrier confinement. For example, the threshold current of ideal quantum dots is predicted to be temperature insensitive. We have investigated the operating characteristics of 1.55 μm InAs/InP (100) quantum dot lasers focusing on their carrier recombination characteristics using a combination of low temperature and high pressure measurements. By measuring the intrinsic spontaneous emission from a window fabricated in the n-contact of the devices we have measured the radiative component of the threshold current density, J rad . We find that J rad is itself relatively temperature insensitive (Fig. 1). However, the total threshold current density, J th , increases significantly with temperature leading to a characteristic temperature T 0 ~72 K around 220 K-290 K. From this data it is clear that the devices are dominated by a non-radiative recombination process which accounts for up to 94% of the threshold current at room temperature (Fig. 1).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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