Kinetic simulation of metal chemical-vapor deposition on high aspect ratio features in modern very-large-scale-integrated processing
Bibliographic record
Abstract
Chemical-vapor deposition of tungsten is extensively used for very-large-scalae-integrated metallization because of its ability to adequately coat the bottom of high aspect ratio features. Despite this, a detailed model of the surface kinetics is not yet widely accepted. Such a model is essential for predicting film coverage over deep topography where fluxes and adsorbate coverage can be very different from those on flat surfaces. By considering the dissociative adsorption of H2 and WF6 and the desorption of H2 and HF molecules, a new surface kinetic model for tungsten deposition is presented. The model includes temperature- and coverage-dependent sticking coefficients of adsorbing reactions, the inhibiting effects of F on H2 adsorption, and multiple reaction pathways. Predictions of the model show reasonable agreement with experimental measurements of H2 partial pressure dependence of tungsten deposition rate over a wide pressure range. Particularly, the model explains the recently observed effect of reduced deposition rate when the H2 pressure becomes comparable to the WF6 pressure. This kinetic model is used by a kinetic thin-film simulator, GROFILMS, to study the W film deposition over high aspect ratio topography. The film growth profile, the coverage of F and H, and the impingement fluxes along the film surface are analyzed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".