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Record W2165186423 · doi:10.1063/1.3702571

A phonon scattering assisted injection and extraction based terahertz quantum cascade laser

2012· article· en· W2165186423 on OpenAlexafffund
E. Dupont, Saeed Fathololoumi, Z. R. Wasilewski, G. C. Aers, S. R. Laframboise, Martin Lindskog, Seyed Ghasem Razavipour, A. Wacker, Dayan Ban, H. C. Liu

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of WaterlooInstitute for Microstructural Sciences
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universität MünchenCMC Microsystems
KeywordsLasing thresholdTerahertz radiationCascadePhononOptoelectronicsRate equationLaserQuantum cascade laserMaterials sciencePopulation inversionScatteringPhonon scatteringPhotonicsPhysicsOpticsCondensed matter physicsQuantum mechanicsChemistry

Abstract

fetched live from OpenAlex

A lasing scheme for terahertz quantum cascade lasers, based on consecutive phonon-photon-phonon emissions per module, is proposed and experimentally demonstrated. The charge transport of the proposed structure is modeled using a rate equation formalism. An optimization code based on a genetic algorithm was developed to find a four-well design in the GaAs/Al0.25Ga0.75As material system that maximizes the product of population inversion and oscillator strength at 150 K. The fabricated devices using Au double-metal waveguides show lasing at 3.2 THz up to 138 K. The electrical characteristics display no sign of differential resistance drop at lasing threshold, which, in conjunction with the low optical power of the device, suggest—thanks to the rate equation model—a slow depopulation rate of the lower lasing state, a hypothesis confirmed by non-equilibrium Green’s function calculations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations76
Published2012
Admission routes2
Has abstractyes

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