Dislocation Conversion and Propagation during Homoepitaxial Growth of 4H-SiC
Bibliographic record
Abstract
Basal Plane Dislocations (BPDs) in SiC are thought to cause degradation of bipolar diodes with blocking voltages > 2kV by triggering the formation and expansion of stacking faults during device operation. Hence, low N doped, thick epitaxial layers without BPDs are urgently needed for the realization of long-term stable SiC bipolar diodes. Such epilayers can be achieved if the conversion of the BPD into another harmless dislocation type is supported by proper epitaxial growth parameters and use of vicinal (off-cut) substrates. In this work, the influence of the substrate’s off-cut angle and of the epilayer thickness on BPD density and surface morphology were investigated. The BPD densities of epilayers grown on 2° and 4° off-cut substrates were very low compared to growth on 8° off-axis substrates. X-Ray Topography has proved that all the Threading Dislocations (TD) propagate from the substrate to the epilayer and that BPDs in the substrate convert to Threading Edge Dislocations (TED) in the epilayer, i.e. the dislocation density (DD) of the substrate determines the epilayer’s DD. The conversion of BPDs is supported by the presence of bunched steps as for growth of thick layers on 2° and 4° off-cut substrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".