High-energy ion (He <sup>+</sup> , Si <sup>++</sup> , Ga <sup>+</sup> , Au <sup>++</sup> ) interactions with PMMA in ion beam lithography
Bibliographic record
Abstract
Abstract Resist-based ion beam lithography has been studied by exposing different species of ions (He + , Si ++ , Ga + and Au ++ ) on 700 and 2000 Å thick poly(methyl methacrylate) (or PMMA) films supported on Si substrates. By comparing the resist sensitivities to different ions and the cross-sectional shapes of the developed features with the simulation outputs from the TRIM (TRansport of Ions in Matter) software, long-chain scissoring in PMMA can be largely attributed to ion-initiated electron cascades (as evaluated by ion energy loss to the electrons) and recoil atom cascades (as evaluated by vacancy distribution in TRIM). The ion-initiated electron cascades contribute more to the resist sensitivity for the lighter ions, while the recoil atom cascades are more important for the heavier ions. A proportional relation between the resist sensitivity and the product of the ion energy loss to electrons and vacancy number is obtained semi-empirically for heavy ions. The He + ion is the only ion species that can travel through and therefore expose the entire 2000-Å thick PMMA resist film, while the heaviest ion, Au ++ , provides the highest resist sensitivity. The effective energy and momentum impartment to the resist by the ion, as revealed by recoil atom cascades and vacancy formation, is important to significantly expanding the material types suitable for ion beam lithography.
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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".