Control of the Ge nanocrystal synthesis by co-implantation of Si+
Bibliographic record
Abstract
The synthesis of Ge nanocrystals (Ge-nc) prepared by 74Ge+ implantation into fused silica followed by co-implantation of Si+ has been investigated for annealing temperatures varying between 850 and 1150 °C. By limiting the thermal diffusion of Ge, co-implanting Si reduces the Ge desorption and affects the growth of Ge-nc, through a Ge trapping mechanism involving the formation of Ge-Si chemical bonds. This is supported by Raman analysis, providing information regarding the material composition for a large variety of fabrication parameters, as well as high resolution scanning electron microscopy imaging, indicating that the average dimension of the synthesized Ge-nc decreases for increasing doses of co-implanted Si. From the spectral analysis of Raman measurements, a systematic evolution of the Ge-Ge, Ge-Si, and Si-Si bond concentrations is characterized as a function of the co-implantation fluences. Two different regimes are clearly identified for each annealing temperature. The first is associated with a linear increase of the residual Ge content with respect to the co-implanted Si, having a slope of ∼1, independent of the annealing temperature. Here, the nucleation of pure Ge-nc and Ge-nc containing Si impurities occurs at similar rates, for co-implanted Si fluences generally lower than the dose of implanted Ge. The second regime occurs for greater co-implantation fluence thresholds that depend on the annealing temperature. It is related to the saturation of the Ge trapping efficiency. In this regime, the formation of Si-Ge bonds dominates, sufficiently reducing the diffusion of Ge to prevent the formation of pure Ge-nc. In addition to limiting the unwanted and critical Ge desorption effects, Si co-implantation is a promising technique for precisely controlling the Ge-nc density, diameter, and uniformity at nanoscale dimensions, parameters which cannot be solely set from the local Ge concentration and/or the annealing parameters due to the high thermal diffusivity of Ge.
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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) | 0.001 | 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".