Chemically Selective Soft X-ray Direct-Write Patterning of Multilayer Polymer Films
Bibliographic record
Abstract
Chemically selective soft X-ray direct-write patterning of trilayer polymer films was performed in a scanning transmission X-ray microscope, extending recent pioneering work on bilayer polymer films. Two trilayer polymer systems were examined: PMMA/PPC/PAN and PMMA/PEC/PAN, where PMMA = poly(methyl methacrylate), PPC = poly(propylene carbonate), PAN = polyacrylonitrile, and PEC = poly(ethylene carbonate). Each polymer layer was selectively patterned by exposure at its characteristic absorption energy: 288.45 eV for PMMA, 290.40 eV for PPC (PEC), and 286.80 eV for PAN. The patterns were visualized by imaging at these same energies. For the PMMA/PPC/PAN trilayer, highly selective patterning was achieved for the PAN and PPC layers, while the selectivity for the PMMA layer was poor. This was significantly improved by replacing PPC with PEC. The trilayer patterning process was simulated from the X-ray absorption spectra of the polymers, the layer order and thicknesses, and the critical doses for damage of each polymer. The simulations give semiquantitative predictions of the experimental contrast, and are a useful tool to find exposure times that optimize pattern contrast. Methods to improve patterning selectivity are discussed. Full color pattern reproduction with ∼150 nm spatial resolution is demonstrated with several high-resolution patterns created in the PMMA/PEC/PAN trilayer film.
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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) | 0.002 | 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".