Evaluating the adhesion of SU-8 thin films using an AlN/Si surface acoustic wave sensor
Bibliographic record
Abstract
A new approach is developed for evaluating the adhesion of SU-8 thin films using a surface acoustic wave (SAW) sensor. The SAW sensor consists of a silicon (Si) substrate coated with a thin aluminum nitride (AlN) film and two sets of inter-digital electrodes (IDT) patterned on the AlN surface. Two sensor configurations are developed in order to evaluate the adhesion of SU-8. In the first configuration the SU-8 layer is patterned on top of a gold film that is deposited on the AlN surface. In the second configuration the gold film is coated with an omnicoat layer prior to patterning the SU-8 film. Omnicoat is an adhesion promoter for SU-8, which is used to increase its adhesion to gold. The frequency responses from both configurations are measured and the shift in the center frequency value is evaluated. The results illustrate that without omnicoat the center frequency shifts to a higher value indicating an increase in the wave velocity. This is because the poor adhesion of the SU-8 layer without omnicoat causes the wave to be more concentrated in the AlN/Si structure and AlN has a higher acoustic wave velocity in comparison to the SU-8 layer. In addition, four SAW sensors operating at four different center frequencies are developed to investigate the change in sensor sensitivity with the increase in center frequency. The results indicate that the sensor sensitivity increases proportionally to the increase in operating frequency. Finally, a theoretical model is developed to calculate the wave dispersion profile for the SU-8/AlN/Si configuration. The interface of the SU-8/AlN layers is modeled as a layer of mass-less springs with stiffness K(N m −3 ). The shifts in the wave dispersion profile at different levels of interface spring stiffness are compared to the experimental values to evaluate the adhesion of the SU-8 layer.
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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 | 0.001 | 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) | 0.000 | 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".