Characterization of thin film deposits on tungsten filaments in catalytic chemical vapor deposition using 1,1-dimethylsilacyclobutane
Bibliographic record
Abstract
Metal filament plays a key role in the technique of catalytic chemical vapor deposition (Cat-CVD) as it serves as a catalyst in dissociating the source gas to form reactive species. These reactive species initiate the gas-phase reaction chemistry and final thin film and nanostructure formation. At the same time, they also react with the metal itself, leading to the formation of metal alloys and other deposits. The deposits on the tungsten filaments when exposed to 1,1-dimethylsilacyclobutane (DMSCB), a single-source precursor for silicon carbide thin films, in the process of Cat-CVD were studied in this work. It has been demonstrated that a rich variety of deposits, including tungsten carbides (W2C and WC), tungsten silicide (W5Si3), silicon carbide, amorphous carbon, and graphite, form on the W filament surfaces. The structural and morphological changes in the tungsten filaments depend strongly on the DMSCB pressure and filament temperature. At 1000 and 2000 °C, the formation of WC and W2C dominates. In addition, a thin amorphous carbon layer has been found at 1500 °C with the 0.12 and 0.24 Torr of DMSCB and a lower temperature of 1200 °C with the 0.48 Torr of DMSCB. An increase in the DMSCB sample pressure gives rise to higher Si and C contents. As a result, the formation of SiC and W5Si3 has been observed with the two high-pressure DMSCB samples (i.e., 0.24 and 0.48 Torr). The rich decomposition chemistry of DMSCB on the W surfaces is responsible for the extensive changes in the structure of the W filament, providing support for the close relationship between the gas-phase decomposition chemistry and the nature of alloy formation on the metal surface. The understanding of the structural changes obtained from this work will help guide the development of efficient methods to solve the filament aging problem in Cat-CVD and also to achieve a controllable deposition process.
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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 | 0.000 |
| 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) | 0.001 | 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".