Mechanical Property Evaluation of Sublimation Agents on Nanostructure Stability in Semiconductor
Bibliographic record
Abstract
Abstract Collapsing nanostructures due to the surface tension of cleaning solutions pose a significant problem during the drying process in semiconductor manufacturing. The sublimation drying method has been proposed as a potential solution, but achieving collapse-free drying has been challenging. This study investigated the correlation between the mechanical properties of sublimation agent thin films and the collapse rate of nanostructures during sublimation drying. First, various mechanical properties — such as Young’s modulus, viscosity coefficient, plateau stress, and rupture stress — of eight sublimation agent thin films were determined by conducting indentation tests using a spherical indenter. Next, sublimation drying of Si substrates with nanostructures was conducted using these sublimation agents, and the pattern collapse rate of the nanostructures was assessed. The study found a correlation between the rate of viscous change during the phase transition of the sublimation agent from liquid to solid and the collapse rate of nanostructures. However, no correlation was found between other mechanical properties and the collapse rate. These results suggest that the viscous change during the solidification of the sublimation agent exerts stress on the nanostructures, leading to pattern collapse. This study highlights the importance of considering the viscous change during sublimation agent solidification for enhancing nanostructure stability in semiconductor manufacturing.
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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".