A Comparative Study of a:SiCN:H Thin Films Fabricated with Acetylene and Methane
Bibliographic record
Abstract
In this paper we present a comparative study of the properties of amorphous hydrogenated silicon carbonitride (SiCN:H) thin films deposited by electron cyclotron resonance plasma enhanced chemical vapor deposition (ECR-PECVD). The elemental composition, growth rate, density, and refractive index values of the SiCN:H thin films were analyzed as functions of flow rates of pure acetylene (C2H2) and methane (CH4) hydrocarbon precursors. The mechanical properties were studied with nanoindentation measurements to compare hardness and Young’s modulus of the SiCN:H thin films deposited with different carbon sources. Variable angle spectroscopic ellipsometry (VASE), elastic recoil detection (ERD), and Rutherford backscattering spectrometry (RBS) were used to determine thin film properties. Higher carbon content in the thin films was achieved by acetylene compared to methane at the same flow rate due to its lower ionization energy during the deposition. Infrared (IR) absorption spectra of the thin films deposited with acetylene precursor were analyzed to determine the correlation between the hydrocarbon flow rate and the intra-molecular bond intensities in the thin films. We found that the major contribution to the hardness comes from hydrogen (H) in the SiCN matrix which makes the films less dense. Carbon improves the hardness, however, H introduced by the hydrocarbon reduces the mechanical strength.
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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".