Numerical Modeling of Nanostructure Deformation During the Phase Change Process of Sublimation Drying
Bibliographic record
Abstract
Abstract To remove residues from the semiconductors, various drying techniques were developed over time, most of which are associated with the deformation of the nanostructure. To deal with this problem, sublimation drying has been introduced wherein the liquid material is first frozen and then sublimed to prevent liquid residues from becoming trapped in the nanostructures. However, the freezing stage can still cause the nanostructure to collapse. The deformation or collapse occurs due to thermal expansion of the sublimating agent during the solidification process. The present study investigates this phenomenon through numerical simulations. Particularly, the freezing process of the sublimating agent, here water, on the silicon (Si) substrate is modeled using COMSOL Multiphysics software. The simulation involves heat transfer in the sublimating material, its thermal expansion, and the mechanical structure of the Si substrate. The results present the displacement of the nanostructure for cells located on the side of the substrate. Furthermore, the variations of stress and temperature during the process are studied. Experimental results are used to validate the numerical model. The model facilitates the design and implementation of the sublimation drying process for 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".