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Record W4391733910 · doi:10.1116/6.0003376

Widely tunable single-mode interband cascade lasers based on V-coupled cavities and dependence on design parameters

2024· article· en· W4391733910 on OpenAlexaff
Z. Wang, Jingli Gong, Jian‐Jun He, Lu Li, Rui Q. Yang, James A. Gupta

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2024
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsNational Research Council Canada
FundersDivision of Electrical, Communications and Cyber SystemsZhejiang UniversityOklahoma Center for the Advancement of Science and TechnologyNational Natural Science Foundation of ChinaUniversity of OklahomaNational Science Foundation
KeywordsMaterials scienceCascadeLaserOptoelectronicsWavelengthSingle-mode optical fiberWaveguideOpticsRidgeSemiconductor laser theoryTunable laserSemiconductorPhysics

Abstract

fetched live from OpenAlex

We report an investigation of V-coupled cavity interband cascade (IC) lasers (ICLs) emitting in the 3-μm wavelength range, employing various waveguide structures and coupler sizes. Type-II ICL devices with double-ridge waveguides exhibited wide tuning ranges exceeding 153 nm. Type-I ICL devices with deep-etched waveguides achieved single-mode emission with wavelength tunable over 100 nm at relatively high temperatures up to 250 K. All devices exhibited a side-mode suppression ratio higher than 30 dB. By comparing the performance of all devices with different sizes and configurations, a good tolerance against the structural parameter variations of the V-coupled cavity laser (VCCL) design is demonstrated, validating the advantages of the VCCL to achieve single-mode emission with wide tunability.

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 score0.003

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)0.0000.000

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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