MVSG GaN-HEMT Model: Approach to Simulate Fringing Field Capacitances, Gate Current De-biasing, and Charge Trapping Effects
Bibliographic record
Abstract
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 VDSresults in transitions in both gate-current (IG) and capacitance (CGG) 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 (IDSat), knee-voltage (VKnee) and output-conductance (gDS). New parameters are added in the model to capture the effects and can be extracted from independent measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".