Experimental and Theoretical Studies on the Solidification of Cyclohexane on Silicon (Si) Substrate
Bibliographic record
Abstract
The semiconductor industry continually seeks optimal methods to avoid nanostructure collapse during the drying process of semiconductor device manufacturing. Despite the effectiveness of sublimation drying, the phenomenon of solidification with nonuniform crystal morphology remains a challenge associated with nanostructure collapse. Therefore, successful implementation of sublimation drying necessitates high-resolution frameworks to predict the freezing behavior of sublimating chemicals at various stages. In this study, we developed a unified and versatile numerical framework to model the solidification process of sublimating agents on silicon (Si) substrates. The enthalpy method was employed to capture the liquid supercooling, equilibrium freezing, and solid subcooling stages, while a two-dimensional (2D) phase-field method was used to capture the crystal growth stage, coupled with a one-dimensional (1D) kinetics model. Classical nucleation theory (CNT) was also calibrated to calculate the nucleation time and temperature. Furthermore, a laboratory-scale experiment was designed to investigate the solidification process of cyclohexane on a bare Si substrate, accurately characterizing the temperature transition and crystal morphology. The developed numerical framework showed excellent agreement with the experimental data regarding the temperature profile and crystal morphology. The results suggested that nonuniform crystal morphology and stochastic nucleation can be delicately controlled by adjusting the cooling conditions of Si substrates, thus preventing the collapse of nanostructure patterns during the semiconductor device manufacturing process.
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 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 | 0.000 |
| 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) | 0.002 | 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".