Plasma-Enhanced Atomic Layer Deposition of Ta(C)N Thin Films for Copper Diffusion Barrier
Bibliographic record
Abstract
Ta(C)N thin films were deposited by plama-enhanced atomic layer deposition using an alternate supply of tertiary amyl imido-tris(dimethylamido) tantalum [TAIMATA] and hydrogen plasma at 230°. Hydrogen plasma acted as an effective reducing agent for TAIMATA. The film thickness/cycle was saturated at 0.132nm/cycle, when the source pulse time exceeds 5 sec. The resistivity of Ta(C)N films was 1000 μΩ-cm, which is higher value as the application of Cu diffusion barrier material. However, the resistivity of TaN films improved with the increase of hydrogen plasma time because of the decrease of Ta3N5 (>106 μΩ-cm) phase formed by insufficient active hydrogen radical to reduce alkyl groups in TAIMATA, the formation of Ta-C (30 μΩ-cm) phase, and the increase of crystallinity of cubic-TaN. By applying higher plasma power, the lattice mismatch between Ta(C)N and CVD-Cu can be minimized. As a result, Ta(C)N films showed an improved adhesion to CVD-Cu.
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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 | 0.000 |
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".