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Record W2564119593 · doi:10.1109/led.2016.2641740

Characterization of Dynamic Self-Heating in GaN HEMTs Using Gate Resistance Measurement

2016· article· en· W2564119593 on OpenAlexafffund
Adrien Cutivet, Flavien Cozette, Meriem Bouchilaoun, Ahmed Chakroun, Osvaldo Arenas, Marie Lesecq, Jean-Claude de Jaeger, Abdelatif Jaouad, François Boone, Hassan Maher

Bibliographic record

VenueIEEE Electron Device Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersDirection Générale de l’ArmementNatural Sciences and Engineering Research Council of CanadaLabEx GANEXAgence Nationale de la Recherche
KeywordsMaterials scienceTransistorOptoelectronicsTransient (computer programming)Thermal resistanceSubstrate (aquarium)Electrical impedancePower semiconductor deviceVoltageElectrical engineeringElectronic engineeringThermalComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

This letter reports on a new method for the characterization of transistors transient self-heating based on gate end-to-end resistance measurement. An alternative power signal is injected to the device output (between drain and source) at constant gate-to-source voltage. The dependence of gate resistance with temperature is used to extract the thermal impedance of the device in frequency domain via electrical measurement. This new method is validated on common-gate AlGaN/GaN high-electron-mobility transistors on Si substrate under different experimental conditions, which demonstrates its potential to provide complete dynamic self-heating models for power transistors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Electron Device LettersSame topicGaN-based semiconductor devices and materialsFrench-language works237,207