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Record W2184787491 · doi:10.1109/eumic.2015.7345110

Thermal performance assessment in AlGaN/GaN structures by microsensor integration

2015· article· en· W2184787491 on OpenAlexaff
Osvaldo Arenas, Elias Al Alam, Ahmed Chakroun, Vincent Aimez, Abdelatif Jaouad, Richard Arès, François Boone, Hassan Maher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceOptoelectronicsReliability (semiconductor)TransistorThermalWide-bandgap semiconductorGallium nitrideEpitaxyDegradation (telecommunications)Substrate (aquarium)Power densityPower semiconductor deviceWork (physics)High-electron-mobility transistorPower (physics)Electronic engineeringElectrical engineeringNanotechnologyMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The high power density in AlGaN/GaN High Electron Mobility Transistors (HEMTs) can notably produce strong self-heating in the device. This effect leads to performance degradation and reliability concerns. Thermal performance of the device is strongly dependent on the epitaxial structure and substrate material. This work puts into perspective the thermal performance of three devices with same dimensions, fabricated on different AlGaN/GaN structures. The evaluation is carried out by the integration of a temperature micro sensor located above the active region.

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 score0.026
Threshold uncertainty score0.710

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)0.0010.000

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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