Efficient modeling of thin film deposition for low sticking using a three-dimensional microstructural simulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern deposition methods for the thin metal films used in very large scale integrated diffusion barriers take advantage of nonunity sticking effects to produce more uniform coatings. Modeling these processes at the feature scale can be challenging due to long execution times which arise from the need to solve self-consistently for the transport of material in the feature. This article presents a methodology for substantially decreasing the execution time for low sticking coefficient simulations. The method is a modification of the traditional sequential Monte Carlo technique in which there is a separation of the transport processes and deposition process. This allows for a normalization of the incident flux magnitude before deposition and a substantial improvement in execution time. The article presents the incorporation of this method into a three-dimensional microstructural simulator, 3D-FILMS. The simulator is first used to confirm the accuracy of the new methodology and then assess its improvement over the more traditional algorithm. Finally, simulations for chemical vapor-deposited W and for sputtered Ti deposition are presented.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it