Luminescence Imaging of Extended Defects in SiC via Hyperspectral Imaging
Bibliographic record
Abstract
Over the past decade, improvements in silicon carbide growth and materials has led to the development of commercialized unipolar devices such as Schottky diodes and MOSFETs, however, much work remains to realizing the goal of wide-scale commercialization of both unipolar and bipolar devices such as pin diodes or IGBTs, for high applications requiring high powers, operating in elevated temperatures or radiation environments or for many fast switching applications. Despite the great strides that have been made in reducing extended and point defect densities during this period, such defects still remain and with the push to lower off-cut angle substrates are in many cases seeing increases in prevalence. Thus, spectroscopic and imaging techniques for locating and identifying these defects are in high demand. Luminescence imaging and spectroscopy have both been utilized heavily in such work, yet simultaneously obtaining corresponding spectroscopic and spatial information from such defects is problematic. Here we report on hyperspectral imaging of electroluminescence from SiC pin diodes, whereby a stack of luminescence images are collected over a wide spectral range (400-900 nm), thereby providing the ability to both image distinct features and identify their corresponding spectral properties. This process is also equally applicable to collecting either photo- or electroluminescence from other materials or devices emitting in either the UV-Vis or NIR spectral range, as well as to reflectance, transmission or other imaging techniques.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".