Efficient modeling of thin film deposition for low sticking using a three-dimensional microstructural simulator
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.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".