Detection of Vacancy Distributions by Decoration with Hydrogen
Bibliographic record
Abstract
A well-known method for the determination of vacancy distributions in silicon is based on the decoration of vacancies with in-diffused platinum and subsequent profiling of the Pt-distribution with DLTS analysis. The vacancy profile can then be correlated with the substitutional Pt profile. However, there are significant drawbacks of this method: The in-diffusion of platinum is a high-temperature step, which already can alter the vacancy profile; moreover, DLTS measurements with a suitable depth resolution are time-consuming and difficult. Therefore, it is proposed in this paper to decorate the vacancies with hydrogen by processes finally resulting in the formation of vacancy- and hydrogen-related donor and acceptor states. The determination of such doping profiles and by this the detection of vacancy distributions can be easily enabled by depth-resolved spreading resistance measurements with a very good spatial resolution. Hydrogen atoms for the decoration of vacancies can be incorporated by plasma exposure or ion implantation.
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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".