Characterization of Near-Interface Traps at Dielectric/SiC Interfaces Using CCDLTS
Bibliographic record
Abstract
Charge trapping at 4H-SiC/dielectric interfaces in 4H-SiC MOS capacitors has been investigated using constant capacitance deep level transient spectroscopy (CCDLTS). The experiments were focused on further understanding of the following aspects related to 4H-SiC/SiO2 interfaces: (i) Origin of near interface oxide traps (NITs), (ii) Effect of interfacial impurity/passivation methods and (iii) Characterization of near-interface oxide traps for different SiC wafer orientations. For the (0001) Si-face 4H-SiC/ SiO2 interface, two types of NITs are typically detected by CCDLTS, named ‘O1’ and ‘O2’ traps with emission activation energies of about 0.15±0.05 eV and 0.39±0.1 eV respectively below the 4H-SiC conduction band. Based on comparison with previous ab initio calculations, the physical identities of these defects have been suggested to be carbon dimers substituted for O dimers (‘O1’) and interstitial silicon atoms (‘O2’) in the near interfacial SiO2 respectively. In this work, it is shown for the first time that such traps are not observed for 4H-SiC/ Al2O3 interfaces, proving that these traps are inherent to the near-interfacial SiO2. In addition, the summary of CCDLTS results for Si-face with different interface trap passivation methods are included in this study. Finally, a comparison is presented for NO annealed (0001) Si-face, (11-20) a-face and (000-1) C-face interfaces that highlight the difference of CCDLTS signatures for the different crystal faces.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".