Investigation of Thin Film Properties of SiCN:H deposited by ECR PECVD with Acetylene and Ethane Hydrocarbon Sources
Bibliographic record
Abstract
Silicon carbonitride (SiCN) thin films have drawn considerable interest among the ternary compounds due to the combination of unique properties such as high hardness, wide band gap, high photosensitivity in the ultraviolet (UV) region and low dielectric coefficient (k). In the last few decades various fabrication methods including reactive sputtering and plasma-enhanced chemical vapour deposition (PECVD) have been intensively studied to achieve SiCN thin films having attractive mechanical, tribological and optoelectronic features. Applications range from hard, wear-resistant coatings, low-k interconnects, UV photodetectors to gas separation membranes [1]. The properties of thin films are not only influenced by the deposition method, which mainly determines the energy of bombarding ions, but also the choice of source gas [2]. In PECVD processes, silicon (Si), carbon (C) and nitrogen (N) can either be introduced separately as silane (SiH4), methane (CH4), and molecular nitrogen (N2) or ammonia (NH3), or alternatively using organic single precursors such as methylsilazanes [3]. In this work we deposited our thin films with the electron cyclotron resonance (ECR) PECVD method, which differs from other PECVD methods due to it is capability of generating a dense, highly ionized plasma (1011 ions/cm3) and ion impingement energies on the substrate as low as 20 eV [4]. We present the compositional and mechanical properties of hydrogenated SiCN (SiCN:H) thin films which were deposited with two different C precursors, acetylene (C2H2) and ethane (C2H6). The stoichiometry, density of the thin film, optical constants, and the bonding structure of SiCN:H thin films as a function of hydrocarbon carbon gas source have been explored. Due to the hydrogen-containing precursors used, the silicon carbonitride films deposited by CVD methods contain a significant amount of hydrogen (H). From Rutherford backscattering spectrometry (RBS), elastic recoil detection (ERD) analysis, quantitative elemental composition distributions including H were found for films deposited with both carbon sources. For further investigation of bonding structure of SiCN:H, Fourier Transform Infrared (FTIR) Spectroscopy and X-ray Photoelectron Spectroscopy (XPS) measurements were performed. Furthermore, we studied the hardness and Young’s modulus by nanoindentation, and variable angle spectroscopic ellipsometry (VASE) measurements were performed to extract optical constants. To interpret the measurements further, nearly stoichiometric silicon nitride and silicon carbide thin films were also prepared. [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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".