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The Family of MVSG Compact Models for High-Voltage Gallium-Nitride Devices

2025· article· en· W4412987463 on OpenAlexafffund
Johan Alant, Runchen Fang, Yijing Feng, Dongyan Xu, Kaiman Chan, Tim Merkin, Ujwal Radhakrishna, Lan Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Waterloo
FundersCollege of Liberal Arts and Human Sciences, Virginia TechUniversity of Waterloo
KeywordsGallium nitrideOptoelectronicsMaterials scienceWide-bandgap semiconductorGalliumVoltageElectrical engineeringComputer scienceEngineeringNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

MIT Virtual-source Gallium-nitride (MVSG) FET compact model was first introduced as a physics-based compact model for radio-frequency (RF) GaN transistors in 2012, and later been selected as a CMC-approved industry standard GaN transistor model. With the rapid evolution and innovation in the field of GaN technology, continuous efforts have been made to improve, update and expand the capabilities of the MVSG models. This paper first provides an overview of the basics of the MVSG compact models. We will then introduce the variety of compact models in the MVSG family for GaN-based transistors, multi-channel diodes, and transmission-line resistors. With the accuracy, scalability and flexibility, the MVSG family of physics-based GaN device compact models provide a solid foundation for the development of Process Design Kit for HV GaN technology, and also serves as useful research vehicles to explore GaN-based device physics, device engineering, and circuit design.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.361

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 head0.021
GPT teacher head0.269
Teacher spread0.247 · 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 designTheoretical or conceptual
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
Published2025
Admission routes2
Has abstractyes

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