Collapse of patterns with various geometries during drying in photolithography: numerical study
Bibliographic record
Abstract
Photolithography is one of the main mass nanoproduction processes. Manufacturing small devices by photolithography is a challenge because of the risk of collapse of patterns during the drying of rinse liquid. Literature models are usable for only long (i.e., LAR, pattern length/spacing greater than 20) two-line parallel patterns. In the current study, a numerical framework is introduced that allows study the collapse of different pattern geometries. In this framework, the rinse interface shape is found using Surface Evolver, and pattern deformation is found using ANSYS through coupled modeling. The results of the new numerical approach, was in agreement with the analytical model results in the range of its applicability (i.e., long two-line parallel pattern). The developed numerical framework was then used to study a few simple geometries where the analytical model was not applicable. One of the findings from the numerical framework results was that, despite the fact that buttresses stiffen the patterns, buttressed patterns deform more owing to the increase in Laplace pressure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".