Atomic Layer Deposition of High-k Films on Graphene Surface
Bibliographic record
Abstract
Graphene has received considerable attentions because of its unique band structure and high electron mobility (>20,000 cm 2 /Vs).Especially, the electronic characteristics of graphene is suitable as a channel material for the high performance metal-oxide-semiconductor field effect transistor.A growth of high-k film is necessary on graphene surface for top-gated graphene transistor.[1]However, graphene has no surface functional groups such as hydroxyl groups (-OH group) except for defects and graphene edges, which caused a failure of high-k film deposition on graphene surface even using state-of-the-art thin film deposition technique, i. e., atomic layer deposition (ALD).Thus, various methods were proposed to deposit dielectric films uniformly on graphene.For example, a growth of dielectric thin film by oxidation treatment after forming a metal seed layer using physical vapor deposition (PVD).In addition, a use of perylene-tetracarboxylic acid (PTCA), and pre-H2O treatment were attempted.Unfortunately, impurities such as the perylene molecules remain on the graphene surface and the pre-H2O treatment and post-oxidation treatment become somewhat complicated.[2,3] In the meantime, enhancement of nucleation sites on graphene surface using ozone degraded the chemical state of graphene because of a defect formation on the graphene.[4]However, the chemical and electrical properties of the graphene must be preserved after the growth of highk films.[5] Here, we propose a novel graphene surface treatment method that allows an enhanced nucleation and growth of high-k thin films by ALD via a graphene surface treatment using trimethylaluminum (TMA) and H2O prior to the deposition of HfO2 film on the graphene.Then, high-k thin films such as Al2O3 and HfO2 were deposited on the surface-treated graphene sample using ALD.These graphene surface treatment methods generate Al2O3 nuclei through physical adsorption on the surface of graphene, which enhanced the growth and nucleation of high-k film through ALD on the graphene surface.As a result, additional defects were not created after surface treatment on graphene and HfO2 ALD.And significant amount of Hf-C bond was observed after HfO2 ALD, which was decreased substantially with surface treatment.Nucleation delay in HfO2 ALD decreases from 70 to 10 ALD cycles with graphene surface treatment and dielectric constant of HfO2 film is higher using the surface treatment (k ~14.5) than that without the surface treatment (k ~5.6).Also, a leakage current (with a capacitor fabrication) was decreased by a factor of 10 5 compared to without surface treatment.The graphene surface treatment method enhanced the nucleation and the electrical properties of the HfO2 film without deteriorating the properties of graphene, thus providing promising opportunities in graphene electronics.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".