Review Article: Hydrogen blistering of silicon: Progress in fundamental understanding
Bibliographic record
Abstract
Abstract The understanding of blistering mechanisms at the fundamental, atomic‐scale level is still not complete, but large strides towards that goal have been made in the last decade. In this issue's Review Article [1] Bernard Terreault gives a comprehensive survey of this progress, discussing the current questions as well as outlining suggestions for future work. The related cover picture shows atomic force micrographs of a variety of surface morphologies obtained by hydrogen ion implantation into silicon followed by rapid thermal annealing. In this particular case, the ions were implanted through PMMA masks of decreasing width (from left to right, 6 μm, 1 μm, 600 nm and 150 nm) [2]. This is but one application of blistering, the most common being ion‐cutting and layer transfer as used in the commercial production of silicon‐on‐insulator wafers. The author is Honorary retired Professor at the EMT (Energy, Materials, Telecom) Center of the Institut National de la Recherche Scientifique (Université du Québec). Another feature included in the current issue is the Editor's Choice contribution ‘Optical and micro‐analytical study of a copper–conjugated polymer composite’ by Kaushik Mallik et al. [3], where the authors investigate a nanoscale fiber network for potential application in microelectronic systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".