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Record W2810549339 · doi:10.1109/ted.2018.2846219

Modeling the Reverse Gate-Leakage Current in GaN-Channel HFETs: Realistic Assessment of Fowler–Nordheim and Leakage at Mesa Sidewalls

2018· article· en· W2810549339 on OpenAlexafffund
Hassan Rahbardar Mojaver, Pouya Valizadeh

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLeakage (economics)Quantum tunnellingMaterials scienceOptoelectronicsTransistorSchottky barrierHeterojunctionElectronElectric fieldCondensed matter physicsElectrical engineeringVoltagePhysicsEngineering

Abstract

fetched live from OpenAlex

A model considering different leakage paths for describing the reverse gate leakage in mesa-isolated polar GaN-channel heterostructure field-effect transistors (HFETs) is presented. For AlGaN/GaN HFETs, it is illustrated that for small negative values of gate-source bias, gate leakage happens from the gate-covered mesa sidewalls to the 2-D electron gas (2-DEG). The bias and temperature dependences of the gate current show that the sidewall path to the 2-DEG is associated with the Poole-Frenkel electron emission. As the gate-source bias becomes more negative, electrons choose a different path to the 2-DEG. In this case, results corroborate that the gate leakage is dominated by the Fowler-Nordheim (FN) direct tunneling process through the III-nitride barrier. The novel contribution of the present analysis is that it postulates that in absence of absolute uniformity, FN tunneling takes place through only a small portion of the surface of the barrier, which boasts the highest electric field or the smallest Schottky barrier height. By applying this hypothesis, the origin of the inconsistencies inherent to the previously presented models in selecting the value of the electron effective mass can be explained.

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 score0.065
Threshold uncertainty score0.886

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 head0.023
GPT teacher head0.300
Teacher spread0.277 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

Explore more

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