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Record W2472981618 · doi:10.1049/joe.2016.0161

GaN high electron mobility transistors: a review from parasitic elements extraction's perspective

2016· review· en· W2472981618 on OpenAlexaff
Anwar Jarndal, Ammar B. Kouki

Bibliographic record

VenueThe Journal of Engineering · 2016
Typereview
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPinchTransistorMaterials scienceExtraction (chemistry)EmbeddingElectronOptoelectronicsComputational physicsComputer sciencePhysicsElectrical engineeringArtificial intelligenceChemistryEngineeringNuclear physicsChromatography

Abstract

fetched live from OpenAlex

In this study, three different parameter extraction approaches for GaN high electron mobility transistors are presented and evaluated. The first approach depends on only cold pinch‐off measurements, the second approach based on cold pinch‐off and forward measurements and the last one uses de‐embedding open structure in addition to unbiased cold measurements at V DS = 0 V and V GS = 0 V. The extracted values using the first two methods have a very good agreement with the extracted values using the last mentioned procedure. This study results verify the applicability of using either pinch‐off measurements or cold unbiased measurements with open structure to extract the device parasitic elements. This accordingly will reduce the cost of using extra de‐embedding structures or high‐current stress forward measurements.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2016
Admission routes1
Has abstractyes

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