Stress Development and Relaxation during Reaction of a Cobalt Film with a Silicon Substrate
Bibliographic record
Abstract
Thin metal films react with silicon substrates to form various metal silicides. The sequence and kinetics of phase formation are still an area of intense research. Comparatively much less work has been done on the issue of stress development caused by the appearance of these new phases. A detailed review of the subject has been done ten years ago. We present here recent results obtained on Pd-Si, Co-Si, Ni-Si and discuss them in the light of what is known today on the elastic and plastic properties of thin films. A simple model published by S. - L. Zhang and F. M. d’Heurle takes into account the simultaneous stress formation due to the reaction and the relaxation of these stresses. It provides a qualitatively satisfying picture of stress evolution at least for the first phase which forms. The model relies on two basic elements: 1) stress formation due to the formation of a new phase, and 2) the stress relaxation mechanism at work in the growing silicide film. The sign of the stress can be understood from the variation in volume that occurs at the growing interface(s). The stress relaxation mechanisms at work in a growing film are complex. They are highly dependent on the microstructure (as we have shown when comparing Pd/Si(001) and Pd/Si (111)) but should be also highly size dependent (e.g. dislocation glide is more difficult in small scale structures). Inhomogeneous plastic relaxation in polycrystalline silicide films may be an important issue.
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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".