Low temperature plasma enhanced atomic layer deposition of conducting zirconium nitride films using tetrakis (dimethylamido) zirconium and forming gas (5% H2 + 95% N2) plasma
Bibliographic record
Abstract
Zirconium nitride (ZrN) has the lowest bulk electrical resistivity and high thermal stability among group IV and V transition metal nitrides, which makes it a promising material for ULSI applications such as a diffusion barrier for Cu interconnects, contact metal in III-V semiconductor devices, and in high density memory structures. Plasma enhanced atomic layer deposition (PEALD) of conducting ZrN thin films using Zr[N(CH3)2]4 and forming gas (5% H2 + 95% N2) plasma is reported in this article. The growth per cycle (GPC) for every deposition was determined from analysis of dynamic in-situ spectroscopic ellipsometry (d-iSE) measurements. An experimental design is proposed for faster determination of ALD growth saturation curves. At substrate temperature of 150 °C, a GPC of 0.10 nm/cycle was observed for self-limiting ZrN PEALD growth. The electrical resistivity of ZrN films deposited on SiO2 substrate was found to be 559.5 ± 18.5 μΩ cm with negligible change in resistivity even after ∼1000 h exposure to air. The metallic behavior of our ZrN films was evident from the free electron dispersion component in dielectric response, the broad band of photoelectron emission across Fermi level and the positive temperature coefficient for resistivity of 0.0088/ °C.
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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".