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WAVSiC: A Physics-Based Compact Model for SiC MOSFETs

2025· article· W7126230899 on OpenAlexaff
Yijing Feng, Johan Alant, Xia Yang, Ryan Fang, Qihao Song, B. Wang, Han Wang, Yuhao Zhang, Ujwal Radhakrishna, L WEI

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsJFETScalabilityModular designFlexibility (engineering)MOSFETSilicon carbideEquivalent circuit

Abstract

fetched live from OpenAlex

The paper proposes WAVSiC, a comprehensive and user-friendly physics-based compact model for SiC MOS-FETs. The model formulation, including the details of the modular approach and the construction of each basic module are explained. The model can automatically account for scalability with device channel-length and other dimensions, as well as critical effects from body diode, JFET depletion, and non-linear drift resistance. Good agreement has been achieved between simulation using fitted model and experimental device characterization and circuit testing, for both a commercial 650V device (at multiple temperatures) and a commercial 1200V device, showing the accuracy, scalability and flexibility of the proposed model. The model has also been validated for computational efficiency, symmetry, robustness, and circuit simulator compatibility, ready for PDK adoption.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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.

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
Published2025
Admission routes1
Has abstractyes

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