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High-temperature terahertz quantum cascade lasers

2021· article· en· W3203939972 on OpenAlexafffund
Boyu Wen, Dayan Ban

Bibliographic record

VenueProgress in Quantum Electronics · 2021
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooOntario Centres of ExcellenceCMC Microsystems
KeywordsTerahertz radiationLasing thresholdQuantum cascade laserOptoelectronicsLaserFabricationCascadePhotomixingMaterials scienceQuantumPhysicsOpticsFar-infrared laserEngineeringTerahertz metamaterials

Abstract

fetched live from OpenAlex

The terahertz (THz) quantum cascade laser (QCL), first demonstrated in 2002, is among the most promising radiation sources in the THz region owing to its high output power and broad frequency coverage from ∼1.3 to ∼5.4 ​THz and sub-terahertz, without and with assistance of external strong magnetic field. The operation of THz QCLs, however, has thus far been limited to applications below room temperature. Recent advances in THz QCL research have principally focused on optimization of quantum design, fabrication, and growth techniques to improve the maximum operating temperature of THz QCLs; these efforts culminated in a recent demonstration of pulse-mode lasing at temperature up to 250 ​K. Research interests continue to be propelled as new maximum lasing temperature record are set, heating up the race to realize room-temperature operation of THz QCLs. This paper critically reviews key achievements and milestones of quantum designs, fabrication techniques, and simulation methods applicable to the high temperature operation of THz QCLs. In addition, this paper provides a succinct summary of efforts in this field to pinpoint the remaining challenges and provide a comprehensive picture for future trends in THz QCL research.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.268
Teacher spread0.260 · 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

Citations64
Published2021
Admission routes2
Has abstractyes

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