Excimer Laser Annealing of Ion-Implanted Silicon: Dopant Activation, Diffusion and Defect Formation
Bibliographic record
Abstract
Minimization of dopant diffusion during electrical activation is a crucial issue in developing sub-50 nm silicon technology. Excimer laser annealing (ELA) in the melting regime is capable of meeting the requirements on shallow junctions in terms of depth, doping concentration and abruptness. However, in order to be successfully employed it has to be demonstrated that ELA can be integrated in a device processing flow. Especially, the compatibility of ELA with other high temperature processing steps such as rapid thermal annealing (RTA) needs to be addressed. In this contribution, we report on phenomena observed for B redistribution that occur during ELA in B-implanted Si and after subsequent RTA. Specific topics to be covered include (i) B build-up at the maximum melt depth during ELA, and (ii) B activation and diffusion beyond the ELA melt depth.
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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".