Mixture of ZEP and PMMA with varying ratios for tunable sensitivity as a lift-off resist with controllable undercut
Bibliographic record
Abstract
A lift-off process is a popular method to pattern metals, especially for the noble metals that are hard to dry-etch. For a “clean” lift-off process, an undercut profile is critical and is commonly achieved by using a bilayer resist stack. A resist with tunable sensitivity is apparently the most desirable, since it offers a controlled amount of undercut when used as the bottom layer, with the top layer being a less sensitive resist. In this study, the authors show that a simple mixture of poly (methyl methacrylate) (PMMA) and ZEP can offer tunable sensitivity by adjusting the ratio of the two resists dissolved in anisole. Higher sensitivity was attained by increasing the ZEP content in the mixture since ZEP is about 3× more sensitive than PMMA. However, the relationship is not a linear one, and the contrast curve for a mixture containing more PMMA (e.g., PMMA:ZEP ratio of 2:1) is closer to that of pure ZEP than to PMMA. For dense line array patterns with a periodicity of 200 and 500 nm, a moderate undercut obtained by using a low ZEP concentration (PMMA:ZEP = 2:1 as the bottom layer, PMMA as the top layer) gave the result for lift-off of 100 nm Cr. While using pure ZEP as the bottom layer, the undercut was often too large that the resist lines collapsed because of capillary force or even completely detached when the adjacent undercut merged together.
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 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".