Compositional effect on the dielectric properties of high-k titanium silicate thin films deposited by means of a cosputtering process
Bibliographic record
Abstract
We report on the successful growth of high dielectric constant (high-k) titanium silicate TixSi1−xO2 thin films of various compositions (0⩽x⩽1) at room temperature from the cosputtering of SiO2 and TiO2 targets. The developed process is shown to offer the latitude required to achieve not only a precise control of the film composition but an excellent morphology (i.e., dense films with low roughness) as well. The Fourier transform infrared and x-ray photoelectron spectroscopy characterizations have evidenced the presence of Ti–O–Si type of atomic environments, which is the fingerprint of the titanium silicate phase. The titanium silicate films are found to exhibit excellent dielectric properties with very low dielectric losses [tan(δ)<0.02] regardless of their composition. The dielectric constant of the films is found to increase with their TiO2 content from 4 (for pure SiO2 films) to 45 (for TiO2). On the other hand, increasing the TiO2 content of the films is also shown to degrade significantly their leakage current. Nevertheless, titanium silicate films with almost equiatomic composition (x∼0.45) are found to exhibit an excellent trade-off between a high-k value (∼18) and low leakage current (∼5×10−7A∕cm2 at 1MV∕cm). Finally, the compositional dependence of the dielectric properties of the TixSi1−xO2 films is discussed in terms of bonding states and optical band gap.
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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) | 0.001 | 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".