Influence of Different Carbon Precursors on Optical and Electrical Properties of Silicon Carbonitride Thin Films
Bibliographic record
Abstract
Silicon carbonitride (SiCN) thin films are widely used for protective hard coatings due to their superior mechanical and chemical properties such as high wear resistance, chemical and thermal stability at high temperatures, and hardness. Another scope of study of SiCN thin films is as the low dielectric constant (LKC) materials, which are desired for replacing silicon oxide (SiO 2 ) in integrated circuits (IC). SiCN owes the interest shown to being an intermediate compound between silicon nitride (Si 3 N 4 ) which is a highly transparent, wide band gap (5 eV) dielectric, and silicon carbide (SiC) with excellent mechanical performance. In this study we present the optical and electrical properties of SiCN:H thin films fabricated by electron cyclotron resonance plasma enhanced chemical vapor deposition (ECR PECVD) by using a mixture of acetylene (C 2 H 2 ) or methane (CH 4 ), silane (SiH 4 ), argon (Ar), and nitrogen (N 2 ) gas precursors. Samples fabricated with two different carbon sources were analyzed and compared regarding their chemical composition as well as their electrical and optical properties. The atomic composition of Si, C, N, O, and H were determined by Rutherford backscattering spectrometry (RBS) and elastic recoil detection (ERD) analysis and the chemical bonds formed in SiCN:H were analyzed through Fourier transform infrared spectroscopy (FTIR). Optical bandgap, index of refraction and extinction coefficient were analyzed by variable angle spectroscopic ellipsometry (VASE) and will be presented for different deposition conditions. The dielectric constants and dielectric breakdown voltages of the thin films were determined through current-voltage (I-V) and capacitance-voltage (C-V) measurements. Lastly, the hardness properties are explained due to varying C and H concentrations.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".