(Invited) Novel Processing for Si-Nanocrystal Based Photonic Materials
Bibliographic record
Abstract
We report a study of novel processing approaches for the formation of silicon nanocrystals for photonic devices. A silicon rich oxide was formed using ion implantation into thermally grown oxides on Silicon and transparent sapphire substrates. These layers were then treated with rapid thermal processing to observe the formation of silicon nanocrystals on a one second to ten minute timescale. Transmission electron microscopy and Raman scattering were used to follow the evolution of the nanocrystal mean diameter with anneal time. Identical samples co-implanted with Erbium demonstrated the widely reported non-resonant energy transfer mechanism from the nanocrystals to the internal energy levels of the erbium. We quantified the sensitization efficiency, IEr/Inc as a function of the Si-NC size by correcting the relative visible and IR luminescence intensities for variations in the nanocrystal density and observed photoluminescence lifetime. We find that the sensitizing efficiency increases exponentially with decreasing Si-NC mean diameter.
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) | 0.003 | 0.001 |
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".