Effects of Dielectric Roughness on Texture of Both PVD Seed Layers and EP Copper
Bibliographic record
Abstract
The ability to control the crystallographic orientation of both the seed layer and the electroplated copper grains is important in obtaining highly reliable Cu interconnects for ultra-large scale integration (ULSI) circuitry. One of the factors controlling film texture is the roughness of the deposition surface. In this paper the effects of dielectric roughness on the crystallographic texture of physical vapor deposited (PVD) copper seed layers and, subsequently, on the texture of electroplated (EP) copper have been investigated. Six relevant interlevel dielectric materials were examined: tetraethyloorthosilicate (TEOS), borophosphosilicate glass (BPSG), silane oxide, silicon nitride, SiLKTM (from the Dow Chemical Corporation), and polysilicon were deposited on 200 mm (001) Si wafers. The RMS surface roughness of these dielectric layers, measured by AFM, ranged from 0.32 nm to 20.51 nm. Texture was analyzed on a dedicated x-ray diffractometer equipped with a two dimensional detector collecting incomplete pole figures with a 1.0 degree resolution in pole figure space. The orientation distribution functions (ODF) were calculated using the arbitrary defined cells method and the volume fractions of major fiber texture components were derived from the ODF. The predominant texture components of the PVD and EP copper were (111) and (511) fiber. It was found that the volume fraction of (111) fiber decreased as the dielectric surface roughness increased. One exception was with the SiLKTM dielectric, which produced significantly weaker texture than other dielectrics with similar surface roughness. The copper films deposited on polysilicon, which possessed the roughest deposition surface of all the dielectric films had a random texture. Finally, a mixture of strong (111) and (511) fiber textures of EP copper was achieved on dielectric underlayers with smoother surfaces. The results demonstrate that the deposition surface roughness plays an important role in establishing the texture in overlying PVD and EP Cu films. The texture of PVD and EP copper may serve as a useful indicator of the underlayer roughness.
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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 | 0.001 |
| 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".