Diffusion of Hydrogen in Proton Implanted Silicon: Dependence on the\n Hydrogen Concentration
Bibliographic record
Abstract
The reported diffusion constants for hydrogen in silicon vary over six orders\nof magnitude. This spread in measured values is caused by the different\nconcentrations of defects in the silicon that has been studied. Hydrogen\ndiffusion is slowed down as it interacts with impurities. By changing the\nmaterial properties such as the crystallinity, doping type and impurity\nconcentrations, the diffusivity of hydrogen can be changed by several orders of\nmagnitude. In this study the influence of the hydrogen concentration on the\ntemperature dependence of the diffusion in high energy proton implanted silicon\nis investigated. We show that the Arrhenius parameters, which describe this\ntemperature dependence decrease with increasing hydrogen concentration. We\npropose a model where the relevant defects that mediate hydrogen diffusion\nbecome saturated with hydrogen at high concentrations. When the defects that\nprovide hydrogen with the lowest energy positions in the lattice are saturated,\nhydrogen resides at energetically less favorable positions and this increases\nthe diffusion of hydrogen through the crystal. Furthermore, we present a survey\nof different studies on the diffusion of hydrogen. We observed a correlation of\nthe Arrhenius parameters calculated in those studies, leading to a modification\nof the Arrhenius equation for the diffusion of hydrogen in silicon.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".