Theoretical and Experimental Investigation on the Use of a Surface Acoustic Wave Sensor for SU-8 Thin-Film Adhesion Characterization
Bibliographic record
Abstract
This paper investigates the application of a surface acoustic wave (SAW) sensor for characterizing the adhesion of SU-8 thin films. The proposed sensor consists of a thin aluminum nitride (AlN) film deposited on a silicon substrate with two sets of interdigital (IDT) electrodes patterned on the AlN film in a delay line configuration. The SU-8 layer is patterned between the input and output IDT electrodes. A theoretical model for calculating the wave dispersion profile for the SU-8/AlN/Si configuration is developed. The effect of changing the adhesion of the SU-8 film on the wave velocity is implemented by assuming the SU-8/AlN interface consists of a layer of distributed massless springs. Different levels of adhesion are accounted for by changing the stiffness of the interfacial springs and the corresponding changes in the wave dispersion profiles are plotted. Four designs for the SAW sensor are fabricated, for each design two sensor configurations are developed to investigate the change in adhesion of the SU-8 film. In the first configuration, a thin gold film is patterned above the AlN film prior to patterning the SU-8 layer. In the second configuration, an Omnicoat layer is patterned above the gold film prior to SU-8 patterning. Omnicoat is an adhesion promoter commonly used to increase the adhesion of SU-8 to gold. The shift in the center frequency values from both configurations is used to characterize the adhesion of SU-8. The theoretical model is then used to find the equivalent spring stiffness values that fit the theoretical to the measured wave velocities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".