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Record W3081091785 · doi:10.1063/5.0013344

Thermal dynamic imaging of mid-infrared quantum cascade lasers with high temporal–spatial resolution

2020· article· en· W3081091785 on OpenAlexafffund
Siyi Wang, Chao Xu, Fei Duan, Boyu Wen, Shazzad Rassel, Man Chun Tam, Z. R. Wasilewski, Lan Wei, Dayan Ban

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooOntario Centres of Excellence
KeywordsCladding (metalworking)LaserLasing thresholdMaterials scienceCascadeOptoelectronicsDissipationActive layerInfraredThermalPhotonicsTemporal resolutionHeat generationSemiconductor laser theoryOpticsChemistryLayer (electronics)NanotechnologySemiconductorPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The time-resolved (TR) temperature profile of actively biased mid-infrared quantum cascade lasers (MIR QCLs) was measured by using charge-coupled-device (CCD)-based thermoreflectance microscopy (TRM) with an ultrafast temporal resolution of 50 ns and a high spatial resolution of 390 nm. Based on the measured TR two-dimensional (2D) temperature profiles, the heat generation and dissipation dynamics within the lasers have been investigated. It is found that the active-region temperature increases quickly to a peak value (up to ∼100 °C above ambient room temperature) within 500 ns upon pulsed current injection of 6 A. The heat dissipation to the top and bottom cladding layers of the device is initially comparable, yet it evolves differently with time. Within 1–2 μs, the heat dissipation to the top cladding is substantially reduced and most of the heat is drained to the substrate through the bottom cladding layer. This constrained heat dissipation results in the elevated temperature in the active region, leading to thermal quenching of lasing operation, which is confirmed by experimental light–current–voltage measurement and theoretical thermal modeling. The TRM is an enabling tool for probing internal thermal dynamics of many active electronic and photonic devices, particularly for those needing special heat and thermal arrangement.

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 score0.025
Threshold uncertainty score0.538

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 head0.008
GPT teacher head0.229
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
Published2020
Admission routes2
Has abstractyes

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