Thin films residual stress profile evaluation using test microstructures: Illustrated on an example of AlN film
Bibliographic record
Abstract
In this study, we investigate the residual stress gradient of aluminum nitride thin film deposited by reactive pulse DC magnetron sputtering technique on a 200 mm diameter silicon wafer with a 1 μm layer of plasma enhanced chemical vapor deposition tetraethylorthosilicate. Stress measurements are obtained using in situ fabricated rotational beam microstructures. The rotating beam moves in response to relief of the residual stress on the connecting arms that experience lengthening or shortening due to compressive or tensile residual stresses, respectively. Various arm-beam connecting joints, separation gaps between the arms, and arm lengths are considered to determine the optimum microstructure for localized residual stress evaluation of the sputtered aluminum nitride. The displacement of the rotating beams with four different arm-beam connecting-joint designs is analytically modeled using COMSOL multiphysics finite element method simulation. The results of the analytical model were found to be in agreement with the results observed through experiments. The stress gradient measurements obtained using the microstructures are compared to the Stoney stress evaluated using a wafer bow technique. Although the predicted Stoney stress shows a 220 MPa tensile residual stress, the observed trend in localized stress values shows that the maximum stress is 280 MPa at the center of the wafer and reduces to about 100 MPa at the edge of the wafer.
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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".