ISPSD 2020 Awards [3 awards]
Bibliographic record
Abstract
A super-junction (SJ) device has been developed to improve the trade-off relationship between the breakdown voltage (V BD ) and specific on-resistance (R on A).A multi-epitaxial growth method had been used for fundamental demonstrations, but this method needs a lot of repetitions of epitaxial growth and implantation in the case of SiC material.A trench-filling epitaxial growth method is expected as a promising alternative, especially for high-voltage devices.In this study, we have established critical fabrication processes for a thick (> 20 μm) and high-aspect-ratio SJ structure.The measured R on A of a 7.8 kV SJ MOSFET was 17.8 mΩ•cm 2 , which corresponds to half the R on A of the state-of-the-art 6.5 kV-class SiC MOSFET with an n-type drift layer.Improvement of trade-off relationship exceeding the 4H-SiC theoretical limit was experimentally demonstrated for the first time.Ryoji Kosugi received the Ph.D. degree in surface science from
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.424 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".