Polymer-based X-ray masks patterned by direct laser writing
Bibliographic record
Abstract
X-ray masks are indispensable tools in deep X-ray lithography (XRL). To date, hardly any fabrication technology can provide affordable and readily available masks with good structure quality. The bottleneck of adequate masks to a large extent limits the widespread use of XRL. In this article, an alternative XRL mask fabrication process is described to significantly improve availability and cost efficiency of XRL masks as key instruments in XRL processing: A 355 nm UV-laser is applied to expose SU-8 resist on an antireflective coating and a copper sacrificial substrate. The voids in this resist template are filled by a two-step electroplating process with sacrificial nickel and 3.6 μm thick gold absorbers. A second SU-8 coat embeds the absorbers, forming the 40 μm mask membrane. This configuration allows for XRL into resists of up to about 200 μm thickness at the SyLMAND beamline, Canada. The absorber structure accuracy is about 1 μm, at smallest tested lateral dimensions of 2 μm isolated features and 500 nm details. Upon release from the substrate, the membrane locally deforms by up to 1.79 μm. PMMA microstructures patterned with such a mask have smooth and vertical sidewalls. The SyLMAND chopper allows one to limit thermal deformations during exposure to the micrometer range: At a beam power of 0.42 W, typical thermal deformations are 0.5 μm–1.4 μm, depending on the layout, and position inaccuracies are about 3.3 μm.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".