Mechanical Modeling of Nano-Structure Collapse in Sublimation Drying
Bibliographic record
Abstract
Abstract Continued miniaturization in semiconductor manufacturing has raised significant concerns about nano-scale pattern collapse, particularly during drying processes. Pattern collapse primarily arises from capillary forces due to residual liquids between nano-structures. Sublimation drying, which avoids the liquid phase, has been proposed to mitigate these forces; however, collapse events still occur, suggesting solidification of the sublimation agent contributes significantly to collapse mechanics. This study investigates the mechanical mechanisms behind nano-structure collapse by integrating sublimation-agent solidification into a theoretical model. Pillar-like patterns were analyzed, identifying interfacial tension at the solid–liquid interface as a primary collapse driver. The collapse force was modeled as inversely proportional to the nth power of spacing between patterns, dependent on viscosity contrast (Δμ) between liquid and solid phases. Seven sublimation agents were evaluated across pillar and line-and-space patterns, correlating maximum stress (σmax) predicted by the model with observed collapse rates. Results revealed a strong correlation for n = 3–4, emphasizing the critical role of interfacial tension and solidification-induced stress. These findings provide practical guidelines for selecting sublimation agents based on viscosity contrasts and optimizing solidification conditions, enhancing the stability and viability of increasingly intricate nano-scale patterning processes in advanced semiconductor manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".