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Effects of Dielectric Roughness on Texture of Both PVD Seed Layers and EP Copper

2005· article· en· W2083196491 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceDielectricTexture (cosmology)Surface finishSurface roughnessWaferComposite materialCopperDiffractometerSilicon nitrideElectroplatingChemical vapor depositionFiberLayer (electronics)OptoelectronicsMetallurgyScanning electron microscope

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.284
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it