(Invited) Comparison of Compositional, Optical and Mechanical Properties of Sicn Thin Films Prepared By Ecr-PECVD with Different Hydrocarbon Precursors
Notice bibliographique
Résumé
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 (10 11 ions/cm 3 ) and ion impingement energies on the substrate as low as 20 eV [4]. A combination of argon diluted silane (SiH 4 ) and molecular nitrogen (N 2 ) are utilized. For carbon incorporation, we explored the influence of methane (CH 4 ), acetylene (C 2 H 2 ), and ethane (C 2 H 6 ) 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 C 2 H 2 and highest for C 2 H 6 . 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, "SiO x N y Waveguides Deposited by ECR-PECVD", M.Eng. thesis, McMaster University, 1993.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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