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Record W2783212696 · doi:10.1109/asicon.2017.8252603

Physics based compact model of GaN HEMT with an efficient parameter extraction flow

2017· article· en· W2783212696 on OpenAlexaff
Hao Zhang, Ahmed Raslan, Slim Boumaiza, Lan Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHigh-electron-mobility transistorParasitic elementParasitic extractionFlow (mathematics)Electronic engineeringPhysicsOptoelectronicsExtraction (chemistry)Current (fluid)Topology (electrical circuits)Computational physicsTransistorElectrical engineeringEngineeringMechanicsVoltage

Abstract

fetched live from OpenAlex

In this paper, a physics based Virtual Source (VS) compact model and its parameter extraction flow is presented for GaN HEMTs targeting RF applications. While the intrinsic current and charge model are developed based on the previously reported Virtual Source model, the gate current model and parasitic element network are proposed based on our applications with a new efficient parameter extraction flow. Both direct current (DC) of drain and gate currents and RF measurements are conducted for model parameter extractions. The new extraction flow uses DC measurement of gate current to extract parasitic resistance elements before optimizing the intrinsic and parasitic element network using S-parameter measurement. This proposed flow provides reliable and accurate extraction for parasitic element values while achieving reasonable resolutions holistically with both DC and RF characteristics.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.405

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 head0.039
GPT teacher head0.289
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations0
Published2017
Admission routes1
Has abstractyes

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