Pulse Transformer-Based Driver Module for SiC MOSFETs in a Single-Switch Converter for Induction Sealing
Bibliographic record
Abstract
Single-switch resonant converter topology is used in induction cooking and cap sealing. Due to the usage of thin non-magnetic material (aluminium foil), converter for cap sealing needs to operate at high frequency to transfer the required power. To maintain zero voltage switching for each load type, the duty cycle of the gate pulse needs to be varied accordingly. To avoid the complexity of changing an application-specific duty cycle, parallel-connected SiC MOSFETs are preferred as a single switch for near-continuous-duty operation. The gate voltage is highly asymmetrical for these devices, and due to a small gate threshold voltage, they are sensitive to ringing in gate signals. This work proposes a novel low-leakage pulse transformer (PT) for a magnetically coupled GDC to drive multiple isolated devices for high current applications like induction sealing. The proposed pulse transformer is cost-effective and can avoid spurious turn-on. The leakage- and self-inductances of the proposed transformer are computed using FEM simulations. The calculated inductances agree with the measured values. By using the proposed PT, the effectiveness of the GDC is demonstrated by using a circuit simulation, and the same is experimentally verified by driving two parallel connected SiC MOSFETs for high-speed offline induction cap sealing applications.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".