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Record W4224282336 · doi:10.3390/photonics9040247

Theoretical Study of Quasi One-Well Terahertz Quantum Cascade Laser

2022· article· en· W4224282336 on OpenAlexaff
Boyu Wen, Dayan Ban

Bibliographic record

VenuePhotonics · 2022
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTerahertz radiationCascadeLasing thresholdQuantum cascade laserOptoelectronicsQuantum wellLaserQuantumMaterials scienceSemiconductorPhysicsOpticsChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Developing a high-temperature terahertz (THz) quantum cascade laser (QCL) has been one of the major challenges in the THz QCL field over recent decades. The maximum lasing temperature of THz QCLs has gradually been increased, arguably by shortening the length of repeating periods of the quantum structure in the device’s active region from 7 wells/14 layers to 2 wells/4 layers per period. The current highest operating temperature of 250 K was achieved in a two-well direct-phonon design. In this paper, we propose a potential and promising novel quantum design scheme named the quasi one-well (Q1W) design, in which each quantum cascade period consists of only three semiconductor layers. This design is the narrowest of all existing THz QCL structures to date. We explore a series of the Q1W designs using the non-equilibrium green function (NEGF) and rate-equation (RE) models. Both models show that the Q1W designs exhibit the potential to achieve sufficient optical gain with low-temperature sensitivity. Our simulation results suggest that this novel Q1W scheme may potentially lead to relatively less temperature-sensitive THz QCLs. The thickness of the Q1W scheme is less than 20 nm per period, which is the narrowest of the reported THz QCL schemes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.981

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)0.0200.000

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 head0.013
GPT teacher head0.269
Teacher spread0.255 · 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
Published2022
Admission routes1
Has abstractyes

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