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Record W2900046749 · doi:10.1088/1674-4926/40/2/022802

2D study of AlGaN/AlN/GaN/AlGaN HEMTs’ response to traps

2019· article· en· W2900046749 on OpenAlex
A. Hezabra, Nora Amele Abdeslam, Nouredine Sengouga, M.C.E. Yagoub

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Semiconductors · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceOptoelectronicsTransistorPassivationConduction bandSubstrate (aquarium)Threshold voltageLayer (electronics)VoltageElectronElectrical engineeringNanotechnology

Abstract

fetched live from OpenAlex

In this work, the effects of GaN channel traps and temperature on the performance of AlGaN/AlN/GaN/AlGaN high electron mobility transistors (HEMTs) on Si (111) substrate, were investigated. 2D simulations carried out using the Silvaco TCAD simulator tool for different drain and gate voltages showed that acceptor-like traps in the channel have a significant influence on the DC and RF characteristics. It was found that deeper acceptors below the conduction band with larger concentration have a more pronounced effect on the transistor performance. Meanwhile, the donor-like traps show no influence. Pulsing the device with different pulse widths and bias conditions, as well as increasing temperature, showed that the traps are more ionized when the pulse is wider or the temperature is higher, which can degrade the drain current and thus the DC characteristics of the transistor. Passivation of the transistor has also a beneficial effect on performance.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.016
GPT teacher head0.266
Teacher spread0.251 · 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