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MVSG GaN-HEMT Model: Approach to Simulate Fringing Field Capacitances, Gate Current De-biasing, and Charge Trapping Effects

2022· article· en· W4322577806 on OpenAlexaff
Ryan Fang, Dylan Ma, Ujwal Radhakrishna, 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 transistorCapacitanceOptoelectronicsTrappingBiasingSchottky diodeSpiceCurrent sourceLogic gatePhysicsComputer scienceMaterials scienceTopology (electrical circuits)Electrical engineeringDiodeCurrent (fluid)VoltageAlgorithmTransistorEngineeringElectrode

Abstract

fetched live from OpenAlex

This paper presents new features of the latest MIT Virtual Source GaN-HEMT (MVSG) model covering fringing field capacitances, gate current de-biasing, and charge trapping effects. These model augmentations capture physical device phenomena observed in industry-devices in a robust, computationally efficient fashion. The features enhance simulation accuracy in both HV and RF-circuit applications and is included in the upcoming standard release of the industry-standard MVSG Verilog-A code. Contributions of this work are: (i) Bias-dependent fringing fields that can describe non-linear device-capacitance behavior due to charge-depletion in drain-access region beyond the last field-plate (FP). Previously, this effect was modeled using a "dummy" non-physical field-plate. (ii) Assignment of gate-source and gate-drain Schottky diodes to internal source/drain-nodes before and after FPs. Charge-depletion in FPs at high V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS</inf> results in transitions in both gate-current (I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</inf> ) and capacitance (C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GG</inf> ) which can be accurately modeled using the new approach. (iii) Enhanced charge-trapping effects that include drain-lag and gate-lag effects, both capture and emission time-constants and their temperature-dependencies. The model is calibrated against measured transient pulsing data and accurately describes dynamic saturation-current (I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSat</inf> ), knee-voltage (V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knee</inf> ) and output-conductance (g <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DS</inf> ). New parameters are added in the model to capture the effects and can be extracted from independent 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.785

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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