Nanoscale Infrared Spectroscopic Characterization of Extended Defects in 4H-Silicon Carbide
Bibliographic record
Abstract
Extended defects in wide-bandgap semiconductors have been widely investigated using techniques providing either spectroscopic or microscopic information. Nano-Fourier transform infrared spectroscopy (nano-FTIR) is a nondestructive characterization method combining FTIR with nanoscale spatial resolution (∼20 nm) and topographic information. Here, we demonstrate the capability of nano-FTIR for the characterization of extended defects in semiconductors by investigating an in-grown stacking fault (IGSF) present in a 4H-SiC epitaxial layer. We observe a local spectral shift of the mid-infrared near-field response, consistent with the identification of the defect stacking order as 3C-SiC (cubic) from comparative simulations based on the finite dipole model (FDM). This 3C-SiC IGSF contrasts with the more typical 8H-SiC IGSFs reported previously and is exemplary in showing that nanoscale spectroscopy with nano-FTIR can provide new insights into the properties of extended defects, the understanding of which is crucial for mitigating deleterious effects of such defects in alternative semiconductor materials and devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".