Behavioral Modelling of the Transient and CdV/dt Induced Turn-on of a Hybrid High Power Switch Unit
Bibliographic record
Abstract
The use of hybrid power switches is a practical and effective method of improving the performance of high power switching devices. An emerging strategy is to combine high power Si IGBTs with SiC MOSFETs to take advantage of the SiC MOSFETs superior switching speed and lower switching losses. While this approach has a number of advantages, when it is used in a half bridge arrangement the possibility of false turn becomes a concern. The SiC MOSFETs improved transient characteristics leads to higher dv/dt during switching, which can induce a spike in the gate voltage of the low side Si IGBT through the parasitic capacitances. This behavior poses a serious risk of lowering efficiencies in operation as well as safety concerns. In this study, a set of behavior models were developed for both the SiC MOSFET and Si IGBT, with the specific target of modelling the transient behavior. These models were then integrated together into a half bridge capable of performing hybrid switching. Lastly, a model was developed to assess the risk of false turn on in the low side IGBT, based on the developed hybrid switching models. The models were limited to only parameters which are readily available on device datasheets, allowing the system to be useful and accessible to designers without the need to perform parameter extraction. The individual switch models accuracy were assessed against a set Spice models, covering a range of power conditions. The induced gate voltage in a synchronous buck converter with two hybrid SiC MOSFET Si IGBT switches was measured and used to verify the validity of the induced voltage model. The design approach for the development of a behavioral transient model and the limitations of the model were explored and discussed.
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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 | 0.000 |
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".