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Record W3034723674 · doi:10.1002/mmce.22291

New <scp>small‐signal</scp> extraction method applied to <scp>GaN</scp> <scp>HEMTs</scp> on different substrates

2020· article· en· W3034723674 on OpenAlex

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

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHigh-electron-mobility transistorPassivationMaterials scienceOptoelectronicsNucleationTransistorGallium nitrideSIGNAL (programming language)NitrideLayer (electronics)VoltageChemistryNanotechnologyComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

In this article, a new extraction technique is proposed to extract the small-signal parameters of gallium nitride (GaN) high electron mobility transistors (HEMTs) on three different substrates namely, Si, SiC, and Diamond. This extraction technique used a single small-signal circuit model to efficiently describe the physical and electrical properties of GaN on different substrates. This technique takes into account any asymmetry between the gate-source and gate-drain capacitances on the asymmetrical GaN HEMT structure, charge-trapping effects, passivation layer inclusion, as well as leakage currents associated with the nucleation layer between the GaN buffer layer and the different substrates. The extracted values were then optimized using the grey wolf optimizer. The proposed technique was demonstrated through a close agreement between simulated and measured S-parameters.

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)0.000
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 score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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