A generic “micro-Stoney” method for the measurement of internal stress and elastic modulus of ultrathin films
Bibliographic record
Abstract
Accurate measurement of the mechanical properties of ultra-thin films with thicknesses typically below 100 nm is a challenging issue with an interest in many fields involving coating technologies, microelectronics, and MEMS. A bilayer curvature based method is developed for the simultaneous determination of the elastic mismatch strain and Young's modulus of ultra-thin films. The idea is to deposit the film or coating on very thin cantilevers in order to amplify the curvature compared to a traditional "Stoney" wafer curvature test, hence the terminology "micro-Stoney." The data reduction is based on the comparison of the curvatures obtained for different supporting layer thicknesses. The elastic mismatch strain and Young's modulus are obtained from curvature measurements of cantilevers before and after the film deposition. The data reduction scheme relies on both analytical and finite element calculations, depending on the magnitude of the curvature. The experimental validation has been performed on ultra-thin low pressure chemical vapor deposited silicon nitride films with thickness ranging between 54 and 133 nm deposited on silicon cantilevers. The technique is sensitive to the cantilever geometry, in particular, to the thickness ratio and width/thickness ratio. Therefore, the precision in the determination of the latter quantities determines the accuracy on the extracted elastic mismatch strain and elastic modulus. The method can be potentially applied to films as thin as a few nanometers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".