Enhancement of phosphorus activation in vacancy engineered thin silicon-on-insulator substrates
Bibliographic record
Abstract
The concentration of vacancy-type defects in a silicon-on-insulator substrate consisting of a 110 nm silicon overlayer and a 200 nm buried oxide has been quantified using variable energy positron annihilation spectroscopy following 300 keV Si+ ion implantation to a dose of 1.5×1015 cm−2 and subsequent annealing at temperatures ranging from 300 to 700 °C. The preferential creation of vacancies (relative to interstitials) in the silicon overlayer leads to a net vacancy-type defect concentration after annealing. Assuming that the defects have a structure close to that of the divacancy we determine the concentration to range from 1.7×1019 to 5×1018 cm−3 for annealing temperatures ranging from 300 to 700 °C. The measured defect concentration is in excellent agreement with that predicted via Monte Carlo simulation. The impact of this net vacancy population on the diffusion and activation of phosphorus introduced by a 2 keV implantation to a dose of 1×1015 cm−2 has been observed. For samples that combine both Si+ and P+ implantations, postimplantation phosphorus diffusion is markedly decreased relative to that for P+ implantation only. Further, a fourfold increase in the electrical activation of phosphorus after postimplantation annealing at 750 °C is observed when both implantations of Si+ and P+ are performed. We ascribe this affect to the reduction in phosphorus-interstitial clusters by the excess vacancy concentration beyond the amorphous/crystalline interface created by the P+ implantation.
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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) | 0.000 | 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".