Enhanced adhesion of electron beam resist by grafted monolayer poly(methylmethacrylate-<i>co</i>-methacrylic acid) brush
Bibliographic record
Abstract
In electron beam lithography, poor resist adhesion to a substrate may lead to resist structure detachment upon development. One popular method to promote resist adhesion is to modify the substrate surface. In this study, the authors will show that a poly(methylmethacrylate-co-methacrylic acid) [P(MMA-co-MAA)] monolayer “brush” can be grafted onto a silicon substrate using thermal annealing that leads to chemical bonding of the P(MMA-co-MAA) copolymer to the hydroxyl group-terminated substrate, followed by acetic acid wash to remove the bulk, unbonded copolymer. The monolayer brush has a thickness of 12 nm. The authors will show that it can greatly improve the adhesion of positive resist, the ZEP-520A, and negative resist polystyrene to bare silicon surfaces, which led to high resolution patterning without resist detachment upon development. The improvement was more dramatic when patterning dense sub-100 nm period grating structures. But the improvement was negligible for an aluminum substrate, because, even without the brush layer, resist adhesion to aluminum is found already to be strong enough to prevent resist structure peeling off. The current simple and low cost method could be very useful when resist adhesion to the substrate for a given developer is weak.
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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 | 0.000 |
| 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) | 0.001 | 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".