Functionalization of Silica Nanoparticles and Native Silicon Oxide with Tailored Boron-Molecular Precursors for Efficient and Predictive <i>p</i>-Doping of Silicon
Bibliographic record
Abstract
Designing new approaches to incorporate dopant impurities in semiconductor materials is essential in keeping pace with electronics miniaturization without device performance degradation. On the basis of a mild solution-phase synthetic approach to functionalize silica nanoparticles, we were able to graft tailor-made boron-molecular precursors and control the thermal release of boron in the silica framework. The molecular-level description of the surface structure lays the foundation for a structure–property relationship approach, which is readily and successfully implemented to dope non-deglazed silicon wafers. As the method does not require an additional oxide capping step and shows minimal risk of carbon contamination, as demonstrated by compositional and electrical characterizations of the wafers, it is perfectly adapted to advanced microelectronics manufacturing processes.
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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 | 0.000 | 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) | 0.000 | 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".