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Record W2541180939 · doi:10.1117/12.826267

Modeling of GaN/AlN heterostructure-based nano pressure sensors

2009· article· en· W2541180939 on OpenAlexaff
Sunil Patil, Niraj Sinha, Roderick Melnik

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsThermionic emissionHeterojunctionMaterials scienceQuantum tunnellingOptoelectronicsLinearityCondensed matter physicsPhysicsElectron

Abstract

fetched live from OpenAlex

We quantify the influence of thermopiezoelectric effects in nano-sized Al<sub>x</sub>Ga<sub>1-x</sub>N/GaN heterostructures for pressure sensor applications based on the barrier height modulation principle. We use a fully coupled thermoelectromechanical formulation, consisting of balance equations for heat transfer, electrostatics and mechanical field. To estimate the vertical transport current in the heterostructures, we have developed a multi-physics model incorporating thermionic emission, thermionic field emission, and tunneling as the current transport mechanisms. A wide range of thermal (0-300 K) and pressure (0-10 GPa) loadings has been considered. The results for the thermopiezoelectric modulation of the barrier height in these heterostructures have been obtained and optimized. The calculated current shows a linear decrease with increasing pressure. The linearity in pressure response suggests that Al<sub>x</sub>Ga<sub>1-x</sub>N/GaN heterostructure-based devices are promising candidates for pressure sensor applications under severe environmental conditions.

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 score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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