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Record W4394994658 · doi:10.1109/jeds.2024.3392174

A Comparative Study on the Effects of Planarity of Access Region on the Low-Frequency Noise Performance of InAlN/GaN HFETs

2024· article· en· W4394994658 on OpenAlexafffund
Yatexu Patel, Pouya Valizadeh

Bibliographic record

VenueIEEE Journal of the Electron Devices Society · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsPlanarity testingNoise (video)Gallium nitrideWide-bandgap semiconductorElectronic engineeringNanotechnologyComputer scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

The low frequency drain noise-current characteristics of metallic-face InAlN/AlN/GaN heterostructure field effect transistors (HFETs) having fin structures only under the gate, while maintaining a planar structure in the access regions, are compared to those of the HFETs having fin structures stretched from source to drain. Evidence indicates that both device types follow the trends of carrier number fluctuation (CNF) with correlated mobility fluctuation (CMF) model of 1/f noise. Accordingly, the noise of the gated channel has been identified as the dominant noise source for both device types. Devices from the former category exhibit improved 1/f noise performance with lower drain noise-current spectral density. This observation could be due to presence of a higher two-dimensional electron gas (2DEG) concentration under the gated-channel overshadowing the carrier number and mobility fluctuations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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 designObservational
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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

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