Exploiting Phonon‐Resonant Near‐Field Interaction for the Nanoscale Investigation of Extended Defects
Bibliographic record
Abstract
Abstract The evolution of wide bandgap semiconductor materials has led to dramatic improvements for electronic applications at high powers and temperatures. However, the propensity of extended defects provides significant challenges for implementing these materials in commercial electronic and optical applications. While a range of spectroscopic and microscopic tools have been developed for identifying and characterizing these defects, such techniques typically offer either technique exclusively, and/or may be destructive. Scattering‐type scanning near‐field optical microscopy (s‐SNOM) is a nondestructive method capable of simultaneously collecting topographic and spectroscopic information with frequency‐independent nanoscale spatial precision (≈20 nm). Here, how extended defects within 4H‐SiC manifest in the infrared phonon response using s‐SNOM is investigated and the response with UV‐photoluminescence, secondary electron and electron channeling contrast imaging, and transmission electron microscopy is correlated. The s‐SNOM technique identifies evidence of step‐bunching, recombination‐induced stacking faults, and threading screw dislocations, and demonstrates interaction of surface phonon polaritons with extended defects. The results demonstrate that phonon‐enhanced infrared nanospectroscopy and spatial mapping via s‐SNOM provide a complementary, nondestructive technique offering significant insights into extended defects within emerging semiconductor materials and devices and thus serves as an important diagnostic tool to help advance material growth efforts for electronic, photonic, phononic, and quantum optical applications.
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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 | 0.000 | 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) | 0.001 | 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".