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Record W3199318925 · doi:10.35848/1347-4065/ac2918

Nanoscale and quantum engineering of III-nitride heterostructures for high efficiency UV-C and far UV-C optoelectronics

2021· article· en· W3199318925 on OpenAlexfundno aff
Xianhe Liu, Ayush Pandey, Zetian Mi

Bibliographic record

VenueJapanese Journal of Applied Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaCollege of Engineering, Michigan State UniversityNational Science Foundation
KeywordsOptoelectronicsMaterials scienceHeterojunctionQuantum dotQuantum efficiencyDiodeQuantum wellLight-emitting diodeCharge carrierLaserSemiconductorNitrideUltravioletNanotechnologyOpticsPhysicsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract We present an overview of some recent advances of nanoscale and quantum engineering of III-nitride heterostructures that are relevant for the development of high efficiency ultraviolet (UV)-C and far UV-C optoelectronic devices, including light emitting diodes and laser diodes. We show that relatively efficient p-type conduction of AlN and Al-rich AlGaN can be achieved in nearly dislocation-free nanostructures, or through in situ Fermi-level control during the growth of epilayers. High luminescence emission efficiency in the deep UV can be realized by exploiting strong quantum confinement of charge carriers, through either the formation of quantum dot-like nanoclusters or monolayer quantum wells. Moreover, the three-dimensional quantum confinement of charge carriers can drastically reduce the transparency carrier density of ultrawide bandgap semiconductors, leading to electrically pumped mid and deep UV laser diodes with ultralow threshold operation.

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 score0.011
Threshold uncertainty score0.561

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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