Nanopatterning of PMMA on insulating surfaces with various anticharging schemes using 30 keV electron beam lithography
Bibliographic record
Abstract
As a low cost and high throughput method for nanoscale pattern replication, step and flash imprint lithography (SFIL) with UV transparent masters is gaining prominence for its potential in photonics and integrated-circuit fabrication. However, dielectric materials appropriate for fabricating nanostructured SFIL masters present a challenge when employing electron beam lithography (EBL) because insulator substrates covered by polymeric resists such as PMMA tend to accumulate charge during EBL exposures, thereby degrading the process. In this work we explore the performance of four different EBL anticharging schemes for nanofabrication of dense arrays of dots having diameters 16–30 nm in PMMA on UV transparent fused silica (FS) substrates. These include overlayers of aluminum or a water-soluble conducting polymer, as well as sandwiching of Al or Cr thin films between the substrate and PMMA. The quality of patterns transferred from PMMA into the underlying metallic layers was analyzed, and the grain size of the metal was found to be the limiting factor determining the edge roughness. The best resolution was attained employing the conducting polymer top-coating. This scheme also involves fewer processing steps. The authors have used this technique for lift-off of Cr and Au as well as Cr masked etch transfer of nanosized patterns into glass substrates for UV-transparent master mold fabrication.
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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".