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 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 | 0.001 |
| 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) | 0.001 | 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".