Atomic layer deposited ultrathin metal nitride barrier layers for ruthenium interconnect applications
Bibliographic record
Abstract
Resistance capacitance time delay in Cu interconnects is becoming a significant factor requiring further performance improvements in future nanoelectronic devices. Choice of alternate interconnect materials, for example, refractory metals, and subsequent integration with underlying barrier and liner layers are extremely challenging for the sub-10 nm nodes. The development of conformal deposition processes for alternate interconnects, liner, and barrier materials are crucial in order for implementation of a possible replacement for Cu interconnects for narrow line widths. In this study, the authors report on ultrathin (∼3 nm) chemical vapor deposition (CVD) grown ruthenium films on 0.5 and 1 nm thick metal nitride (TiN, TaN) barrier layers deposited via atomic layer deposition (ALD). Using scanning electron microscopy, the authors determined the effect of the underlying barrier layer on the coverage of the ruthenium overlayer. The authors utilized synchrotron x-ray diffraction with in situ rapid thermal annealing to investigate the thermal stability of the barrier layers and determine the effective activation energies of barrier failure leading to ruthenium monosilicide formation. For Ru films deposited directly on Si and on 0.5 nm MN (M = Ti, Ta) covered Si substrates, silicide formation proceeds via a two-step crystallization process involving lateral nucleation above ∼440 °C followed by thickening of the ruthenium monosilicide layer above ∼520 °C. This silicidation temperature of ∼440 °C could be potentially problematic in back-end-of-the-line (BEOL) processing since it is close to the typical thermal budget used. However ∼1 nm thick ALD MN (M = Ti, Ta) was found to be adequate to block silicide formation up to ∼580 and ∼620 °C for TiN and TaN, respectively, and also aided in superior coverage of the CVD ruthenium overlayer (>90%). The results reported here might be useful to ascertain annealing temperature and time for BEOL process and integration optimization without reaching a state where ruthenium silicides start forming.
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 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".