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Record W1967887035 · doi:10.1109/jphot.2015.2416343

Effect of Carrier Leakage on Optimal AR Coatings in Midinfrared Interband Cascade Lasers

2015· article· en· W1967887035 on OpenAlexafffund
Jeyran Amirloo, Simarjeet S. Saini, M. Dagenais

Bibliographic record

VenueIEEE photonics journal · 2015
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and Innovation
KeywordsAnti-reflective coatingMaterials scienceCoatingSlope efficiencyLaserOptoelectronicsCascadeLeakage (economics)Semiconductor laser theoryOptical coatingCarrier lifetimeReflectivityOpticsSiliconNanotechnologyFiber laserSemiconductorPhysicsChemical engineering

Abstract

fetched live from OpenAlex

Variation of leakage current in interband cascade lasers (ICLs) with different carrier concentrations achieved by coating the facet with antireflections (AR) coating was experimentally studied. Single-layer Al2O3 and double-layer ZnS-YF3, ZnS-SiO2, and Ti2O5-SiO2 AR coatings are applied to midinfrared (mid-IR) ICL devices to achieve reflectance ranging from 0.15 to 7 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . By monitoring the laser performance before and after coating, it was observed that, for lower reflectivity coatings, the leakage current appreciably rises with increasing carrier concentration, thus diminishing the slope efficiency improvements and, in fact, degrading the slope efficiency at very low reflectance. It was observed that the ratio of leakage to threshold current could increase by 17% for high carrier concentrations at very low AR coating values. The results also allow for experimental optimization of AR coatings for increased slope efficiency in mid-IR ICLs. Therefore, in this paper, we propose an optimal value for the AR coatings in order to maximize the power in ICLs.

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)0.001
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 score0.014
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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