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Record W2279103909

EVOLUTION OF MICROSTRUCTURE IN TI-TA BILAYER THIN FILMS ON POLYCRYSTALLINE-SI AND SI(001)

2004· article· en· W2279103909 on OpenAlexaff
Ahmet S. Özcan, Karl Ludwig, C. Lavoie, Soumendra N. Basu, Cedrik Coia, C. Cabral, Kenneth P. Rodbell, J. M. E. Harper

Bibliographic record

VenueJournal of Vacuum Science & Technology B Microelectronics and Nanometer Structures Processing Measurement and Phenomena · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceSilicideMicrostructureCrystalliteTransmission electron microscopyTexture (cosmology)BilayerVolume fractionThin filmAnalytical Chemistry (journal)SiliconLayer (electronics)DiffractionPole figureScatteringCrystallographyComposite materialMetallurgyOpticsNanotechnologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Abstract We have studied the formation of titanium silicides in the presence of an ultra-thin layer of Ta, interposed between Ti and Si, using in situ X-ray diffraction (XRD), resistance measurements, elastic light scattering and exsitu transmission electron microscopy. On both poly-Si and Si(001) substrates the Ta thickness was varied from 0 to 1.5 nm while the Ti thickness was held constant at ∼27 nm. The time-resolved XRD shows that the volume fraction of the C40 and metal-rich silicide phases grows with increasing Ta layer thickness. Among the Ta thicknesses we examined, 0.3 nm is the most effective in lowering the C49–C54 transformation temperature. Ex situ texture analysis shows that the C54 disilicide film is predominantly (010) textured for the Ti/0.3 nm Ta sample on both poly-Si and Si(001). The final C54 texture is significantly different for Ta layers thinner or thicker than the optimal 0.3 nm. This suggests that the most effective thickness for lowering the C54 formation temperature could be related to the development of a strong (010) texture.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.095
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

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