Optical and Electrical Properties of ECR-PECVD Grown SiCN Thin Films
Bibliographic record
Abstract
Recent progress in scaling down the metal-oxide semiconductor (MOS) devices accompanied limitations arising from quantum effects, which leads to a search for new materials. Silicon oxide (SiO 2 ) thin films have been a favourable dielectric for more than 4 decades. Their usage have become impractical due to the power dissipation and delay in interconnects. To increase the electrical performance, low extinction coefficient (low-k) dielectric materials are being developed as a substitute to SiO 2 . Those materials draw considerable attraction due to their advances in the integrated circuit technology to increase the transistor density. Among numerous materials, silicon carbonitride (SiCN), which is an intermediate compound between silicon nitride (SiN) and silicon carbide (SiC), has become noteworthy with its unique properties as low-k, robustness, high thermal stability, and wide bandgap of 2.2eV- 5eV 1 . In this work we present the optical, structural, and electrical analysis of SiCN thin films grown using electron cyclotron resonance plasma enhanced chemical vapor deposition (ECR-PECVD). The influence of the precursors (SiH 4 , C 2 H 2 , and N 2 gases) on the composition and electronic structure of thin films which were characterized by Rutherford backscattering spectroscopy (RBS) and Fourier transmission infrared spectroscopy (FTIR). Furthermore, current-voltage characteristics were studied in dark and illuminated environment at room temperature. We studied the variation of photodiode characteristics with the thin film compositions. These results were also compared with the SiCN thin films growth using identical parameters except for the carbon source (CH 4 gas) which has been reported in earlier studies 2 . Our findings showed that the hydrogen content influenced the optical coefficients of these two set of samples. Index of refraction and extinction coefficients were characterized by variable angle spectroscopic ellipsometry (VASE). In addition to our works on developing low-k matrix to enhance the performance of the integrated circuits, the wide gap feature of SiCN thin films enable us to consider the ultraviolet (UV) photo responsivity effect which is studied for photodetector device application. References: [1] B.P. Swain, N.M. Hwang, Appl. Surf. Sci. 254 (2008) 5319 [2] Z. Khatami, P.R.J. Wilson, J.Wojcik, P. Mascher, Thin Solid Films 622 (2017) 1–10
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 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".