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Record W2547061557 · doi:10.1139/cjp-2016-0304

Piezoelectric interaction in controlling the effective electron temperature and the non-ohmic mobility characteristics in GaN and other III–V compounds at low lattice temperature

2016· article· en· W2547061557 on OpenAlexvenueno aff
Biswajit Das, A. N. Basu, Jayoti Das, D.P. Bhattacharya

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
Fundersnot available
KeywordsOhmic contactCondensed matter physicsPhysicsElectronIndiumLattice (music)PiezoelectricityElectric fieldPhononElectron mobilitySemiconductorOptoelectronicsElectrodeQuantum mechanics

Abstract

fetched live from OpenAlex

In a compound semiconductor that lacks inversion symmetry, the free electrons interact simultaneously with the piezoelectric and acoustic phonons. This combined interaction principally controls the electrical transport at low lattice temperatures. Again, at low temperatures, the electrons in some of the compounds may be significantly perturbed for comparatively low fields, say, even for a fraction of a Vcm −1 or so, which effectively seems to be high enough, and the material exhibits electrical nonlinearity. Such a perturbed ensemble then attains a field dependent effective electron temperature T e , which exceeds the lattice temperature T L . The relative importance of the piezoelectric interaction in controlling the field dependence of the effective electron temperature, and therefrom, the non-ohmic mobility characteristics have been analyzed here under the condition of low lattice temperature. The numerical results obtained for InSb, InAs, and GaN are studied in detail. When compared with the experiments, the results here seem to give the same qualitative picture with respect to the variation of the non-ohmic mobility with the electric field for the indium compounds. The results, being interesting, stimulate further work in the same field.

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 score0.381
Threshold uncertainty score0.414

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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