On the Frequency Dependence of the Gate Switching Instability in Silicon Carbide MOSFETs
Bibliographic record
Abstract
Silicon carbide power metal-oxide semiconductor field-effect transistors (MOSFETs) are suitable for more compact and energy efficient electric power conversion, pushing forward numerous key technologies in emission-free mobility and green power generation. These applications require fast gate switching up to hundreds of kilohertz. Typically, to assure a clear turn-off state of the electron channel, a negative turn-off gate bias is used in conjunction with a positive turn-on gate bias. Recently, several reports have revealed a new and so far unknown degradation mechanism that emerges during such operation conditions in apparently all commercial silicon carbide MOSFETs. As this mechanism arises upon gate switching, the terms gate switching instability (GSI) and gate switching stress (GSS) for the mechanism and the associated stress, respectively, have been introduced.Here, we show that this degradation mechanism does not depend on the used frequency up to at least 2 MHz, but that it is actually related to the cumulative number of switching cycles. For this purpose, we performed measurements at various frequencies comprising ultra-fast in-situ measurements of the threshold voltage and pre- and post-stress characterization by impedance analysis. The results are important both for understanding the underlying physics and for developing a correct methodology for industrial device qualification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".