New Formulas for Evaluation of Cyclohexanol Solidification on Substrates with Surface Nanostructures
Bibliographic record
Abstract
In semiconductor manufacturing, the sublimation drying process is crucial but poorly understood-particularly regarding the solidification of agents such as cyclohexanol on Si substrates. This knowledge gap results in inconsistent film properties and risks such as structural collapse. To address this critical gap in knowledge, the present study focused on an in-depth examination of the nucleation behavior exhibited by cyclohexanol during its cooling and solidification on Si substrates. Using a digital camera (GoPro10), the solidification process in experiments was recorded for a range of cooling rates and using substrates with distinct surface patterns. To evaluate temporal changes in crystal nucleation, video images were visually checked, and the temporal changes in the number of nuclei were examined. For a more quantitative analysis, the least-squares method was successfully employed to correlate mathematical equations to time-dependent nucleation data. Interestingly, the outcomes revealed significant correlations between the nucleation rate, cooling rate, and substrate pattern. In summary, this research offers a robust experimental framework for understanding the complex solidification behavior of cyclohexanol on Si substrates. The study contributes both qualitative and quantitative analyses, enriching our understanding of the variables that govern the solidification process, which has significant implications for enhancing the overall reliability and efficiency of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".