On the Influence of Hydrocarbon Precursors and Elemental Composition on the Dielectric Properties and Conduction Mechanism of Hydrogenated Silicon Carbonitride Thin Films
Bibliographic record
Abstract
Ternary compound thin films have drawn interest since intermediate forms can be developed for materials with tunable properties. Among them, thin films of silicon carbonitride (SiCN) 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 area of application of SiCN thin films is as low dielectric constant materials, which are desired for replacing silicon oxide (SiO 2 ) or silicon nitride (Si 3 N 4 ) in integrated circuits (IC) and charge trapping capacitors. In general, SiCN is an intermediate compound between Si 3 N 4 which is a highly transparent, wide band gap (5 eV) dielectric, and silicon carbide (SiC) with high mechanical durability [1]. One of the most common techniques for the fabrication of SiCN thin films is plasma enhanced chemical vapour deposition (PECVD) with alkylsilazane precursors for high composition control [2]. Recently, we reported the compositional and mechanical properties of SiCN:H thin films, which were deposited using electron cyclotron resonance (ECR) PECVD with two different hydrocarbon precursors, acetylene (C 2 H 2 ) and methane (CH 4 ) [3, 4]. In the present work, we explore how film composition affects the electrical behaviour of the thin films. More specifically, we investigate the dielectric properties of SiCN:H thin films and the charge transportation and trapping mechanisms for different stoichiometries and film densities, as a function of the hydrocarbon precursor. Metal-insulator-semiconductor (MIS) capacitor type structures were formed on p-type (100) silicon substrates. Following the deposition, aluminum (Al) gate electrodes were sputtered for current-voltage (I-V) and capacitance-voltage (C-V) measurements operated at 1 MHz. The results were correlated with composition and density of SiCN:H thin films obtained from Rutherford backscattering spectrometry (RBS), elastic recoil detection (ERD), and variable angle spectroscopic ellipsometry (VASE) measurements. Finally, finite element modeling was performed by COMSOL to compare the capacitance characteristics in MIS structures of the thin films. [1] L.C Chen, C.K. Chen, S.L Wei, D.M Bhusari, K.H. Chen, Y.F. Chen, Y.C. Jong and Y.S. Huang. Crystalline silicon carbon nitride: A wide band gap semiconductor. Applied Physics Letters, 72(19), pp.2463-2465 (1998). [2] S. Bulou, L. Le Brizoual, P. Miska, L. de Poucques, R. Hugon, M. Belmahi, J. Bougdira. The influence of CH4 addition on composition, structure and optical characteristics of SiCN thin films deposited in a CH4/N2/Ar/hexamethyldisilazane microwave plasma. Thin Solid Films, 520(1), 245-250 (2011). [3] A. Abdelal, Z. Khatami and P. Mascher. Influence of Different Carbon Precursors on Optical and Electrical Properties of Silicon Carbonitride Thin Films. ECS Transactions, 97(2), 59 (2020). [4] Z. Khatami, G. B. F. Bosco, J. Wojcik, T. R. Tessler, P. Mascher. Influence of Deposition Conditions on the Characteristics of Luminescent Silicon Carbonitride Thin Films. ECS Journal of Solid-State Science and Technology, 7(2), N7 (2018).
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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 | 0.000 |
| 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) | 0.000 | 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".