Calculation of Printed Circuit Board Power-Loop Stray Inductance in GaN or High <italic>di/dt</italic> Applications
Bibliographic record
Abstract
This paper is concerned with the determination of parasitic inductance values in very fast switching power devices. To keep improving today's power converters, new technologies are studied, which exhibit very low switching times. The wide-bandgap semiconductors are among the key aspects of these improvements. Thanks to their internal properties, they allow very fastdi/dtanddv/dtwith very small footprint. Stray loop inductance needs to be kept low, as it creates high peak voltage upon switching of a transistor with fastdi/dt. In particular, the stray inductance value with respect to the loop size and geometry needs to be calculated accurately at the design stage of the power converters. This paper analyzes three loop geometries and studies one with minimized stray inductance and optimal current distribution. An analytical method is proposed, which uses the Biot-Savart law for an accurate analytical estimation of the magnetic field intensity in the selected geometry, leading to inductance calculation. A comparison between the classical two-plate inductance estimation formula and the proposed stray inductance estimation is presented, proving more accurate value with the method proposed in this paper. Finally, an experiment has validated the new inductance estimation formula.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".