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Record W2791384109 · doi:10.1088/1361-6463/aab1e4

A dominant electron trap in molecular beam epitaxial InAlN lattice-matched to GaN

2018· article· en· W2791384109 on OpenAlexafffund
Ayush Pandey, Aniruddha Bhattacharya, Shaobo Cheng, Gianluigi A. Botton, Zetian Mi, P. Bhattacharya

Bibliographic record

VenueJournal of Physics D Applied Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcMaster University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsMolecular beam epitaxyMaterials scienceTransmission electron microscopyDopingHigh-resolution transmission electron microscopyElectronDiodeBand gapEpitaxyScanning transmission electron microscopyOptoelectronicsMolecular physicsChemistryPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Abstract Deep levels in lattice-matched undoped and Si-doped InAlN/GaN grown by plasma-assisted molecular beam epitaxy have been identified and characterized by capacitance and photocapacitance measurements. From x-ray diffraction, reflectance measurements, electron energy loss spectroscopy and high-resolution transmission electron microscopy it is evident that the material has two distinct phases with different compositions. These correspond to In compositions of 18.1% and 25.8%, with corresponding bandgaps of 4.6 eV and 4.1 eV, respectively. The lower bandgap material is present as columnar microstructures in the form of quantum wires. A dominant electron trap with an activation energy of 0.293 ± 0.01 eV, a small capture cross-section of (1.54 ± 0.25) × 10 −18 cm 2 , and density increasing linearly with Si doping density is identified in all the samples. The characteristics of the electron trap and variation of diode capacitance are discussed in the context of carrier dynamics involving the dominant trap level and the quantum wires.

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 categoriesMeta-epidemiology (narrow)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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