Raman-based mapping and depth-profiling of the relaxation state in amorphous silicon
Bibliographic record
Abstract
We show that the micro-scale variations in the relaxation state of amorphous silicon (a-Si) can be well-identified by Raman mapping over hundreds or thousands of μm2 in 1–2 h. Pure and relaxed a-Si is obtained by self-implantation in crystalline silicon (c-Si) followed by anneal at 500 °C. It is then locally re-implanted over micro-sized patterns to produce unrelaxed a-Si zones. Raman mappings are obtained by pointwise confocal μ-Raman and hyperspectral Raman imaging. We also measure the depth profiles of the relaxation state in re-implanted a-Si by scanning the edge of a re-implanted sample. We infer from the depth profiles that the minimal damage dose to fully de-relax a-Si is 0.04 displacements per atoms, which is an order of magnitude smaller than the fluence needed to fully amorphize c-Si.
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) | 0.001 | 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".