Effect of molecular weight distribution on e-beam exposure properties of polystyrene
Bibliographic record
Abstract
Polystyrene is a negative electron beam resist whose exposure properties can be tuned simply by using different molecular weights (Mw). Most previous studies have used monodisperse polystyrene with a polydispersity index (PDI) of less than 1.1 in order to avoid any uncertainties. Here we show that despite the fact that polystyrene's sensitivity is inversely proportional to its Mw, no noticeable effect of very broad molecular weight distribution on sensitivity, contrast and achievable resolution is observed. It is thus unnecessary to use the costly monodisperse polystyrene for electron beam lithography. Since the polydispersity is unknown for general purpose polystyrene, we simulated a high PDI polystyrene by mixing in a 1:1 weight ratio two polystyrene samples with Mw of 170 and 900 kg mol(-1) for the high Mw range, and 2.5 and 13 kg mol(-1) for the low Mw range. The exposure property of the mixture resembles that of a monodisperse polystyrene with similar number averaged molecular weight Mn, which indicates that it is Mn rather than Mw (weight averaged molecular weight) that dominates the exposure properties of polystyrene resist. This also implies that polystyrene of a certain molecular weight can be simulated by a mixture of two polystyrenes having different molecular weights.
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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 | 0.001 |
| 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".