Effect of surface treatments/coatings and soft bake profile on surface uniformity and adhesion of SU-8 on a glass substrate
Bibliographic record
Abstract
We present the effect of surface treatments/coatings and soft bake temperatures aimed at improving adhesion and surface uniformity of SU-8 on glass substrates. While the adhesion strength of SU-8 to metal layers on glass and silicon has been previously investigated, our research examines the influence of additional surface treatments (RCA, Acetone/IPA rinse) and coatings (fresh/one-day-aged Ti, fresh/one-day-aged Cr, SU-8 2005®) on adhesion strength as well as surface uniformity for 100 μm thick SU-8 films. Additionally, we vary the soft bake times and temperatures while keeping all other process parameters constant, to correlate adhesion strength with surface uniformity of SU-8 films for each surface modification. We have found that for all surface treatments/coatings, a soft bake temperature of 65°C for 90 minutes yielded a more uniform SU-8 film (σ = 5.18 μm) as compared to the manufacturer-recommended soft bake temperature of 95°C (σ = 12.66 μm) for 30 minutes. Consequently, a more uniform SU-8 film provided excellent adhesion strength (> 2 MPa, as determined by stress testing using an Instron® microtester) for both metallic seed layers while the adhesion strength of films baked at 95°C was determined to be < 0.5 MPa. This study, for the first time, has been able to quantitatively determine the adhesion strength of SU-8 films on different seed layers deposited on glass substrates, for varying soft bake temperatures.
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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) | 0.001 | 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".