(Invited) Comparison of Compositional, Optical and Mechanical Properties of Sicn Thin Films Prepared By Ecr-PECVD with Different Hydrocarbon Precursors
Bibliographic record
Abstract
Silicon carbon nitride (SiCN) ternary compounds present remarkable mechanical strength, bandgap tunability, optical responsivity in the UV region, and dielectric performance in microelectronics due to the combined features of silicon nitride (SiN), silicon carbide (SiC), and carbonitride (CN) [1]. The SiCN compounds can be formed using fabrication methods such as physical vapor deposition (PVD), chemical vapor deposition (CVD), and chemical synthesis. Successful SiCN thin films fabricated with different techniques and their characteristics have been reported extensively in the literature; however, the influence of hydrocarbon gas precursors has not drawn the same amount of attention for SiCN. Chemical, physical, and mechanical properties of thin films are determined by the growth parameters and the choice of sources used, like the organic single-molecule (methylsilazanes) or highly pure individual gas precursors [2,3]. The chemical vapor deposition systems mainly affect the energy of bombarding ions. Plasma-enhanced CVD has been commonly used for thin-film depositions since it provides low deposition temperature, high purity, good step coverage, and easy control of reaction parameters. Our work focuses on the electron-cyclotron resonance plasma-enhanced chemical vapor deposition (ECR PECVD) method to fabricate SiCN thin films. This method differs from other PECVD methods because it can generate a dense, highly ionized plasma (1011 ions/cm3) and ion impingement energies on the substrate as low as 20 eV [4]. A combination of argon diluted silane (SiH4) and molecular nitrogen (N2) are utilized. For carbon incorporation, we explored the influence of methane (CH4), acetylene (C2H2), and ethane (C2H6) hydrocarbon gas precursors on SiCN thin film properties. The stoichiometry, density of the thin film, optical constants, and the bonding structure of SiCN thin films as a function of hydrocarbon carbon flow rates are presented. Due to the hydrogen-containing precursors used, the silicon carbonitride films deposited by CVD methods contain a significant amount of hydrogen (H), lowest for C2H2 and highest for C2H6. Nearly stoichiometric silicon nitride and silicon carbide thin films were also prepared to interpret the measurements further. From Rutherford backscattering spectrometry (RBS) and elastic recoil detection (ERD) analysis, quantitative elemental composition distributions including H were found for films deposited with both carbon sources. For further investigation of the bonding structure of SiCN, Fourier Transform Infrared (FTIR) Spectroscopy was performed. Furthermore, we studied the hardness and Young’s modulus by nanoindentation, and optical constants were measured by variable angle spectroscopic ellipsometry (VASE). [1] C.W. Chen, C.C. Huang, Y.Y. Lin, L.C. Chen, K.H. Chen, W.F. Su, Optical prop- erties and photoconductivity of amorphous silicon carbon nitride thin film and its application for UV detection, Diamond Relat. Mater. 14 (3-7) (2005) 1010–1013. [2] Schwarz-Selinger, T., Von Keudell, A., & Jacob, W. (1999). Plasma chemical vapor deposition of hydrocarbon films: The influence of hydrocarbon source gas on the film properties. Journal of Applied Physics, 86(7), 3988-3996. [3] V.I. Ivashchenko, A.O. Kozak, O.K. Porada, L.A. Ivashchenko, O.K. Sinelnichenko, O.S. Lytvyn, T.V. Tomila, V.J. Malakhov, Characterization of SiCN thin films: experimental and theoretical investigations, Thin Solid Films 569 (2014) 57–63. [4] M. G. Boudreau, "SiOxNy Waveguides Deposited by ECR-PECVD", M.Eng. thesis, McMaster University, 1993.
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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) | 0.002 | 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".