Characterization of Ultrathin Electroless Barriers Grown by Self-Aligned Deposition on Silicon-Based Dielectric Films
Bibliographic record
Abstract
A self-aligned, electrochemically integrated seeding/plating approach is developed for fabricating patterns of cobalt- (or nickel-) based metallic barriers and copper films selectively on silicon-based dielectric (hybrid siloxane-organic polymer and films using electroless plating. High-resolution X-ray absorption spectroscopy, transmission electron microscopy, atomic force microscopy, and grazing-incidence X-ray diffractometry indicate that, after they have been appropriately pretreated by a gaseous plasma or and basic aqueous solutions that contain sufficient amounts of peroxide hydrogen the dielectric films can adsorb highly populated metallic (nickel or cobalt) precipitates of sizes between 2 and 4 nm, which catalyze the deposition of ultrathin (⩽20 nm) barriers. The barriers are initially highly resistive and contain ultrafine (3-5 nm) crystallites embedded in amorphous-like, 20 nm grains, but become highly conductive (50-80 μΩ-cm) following optimal annealing at temperatures ⩾470°C because of crystallization, grain growth, and precipitation of Finally, the capacity of this method to fabricate "self-aligned" patterns of barrier and copper is established, and the adhesion strength and effectiveness of the barriers against copper's diffusion/drift are quantified. The importance of the plasma pretreatment and the use of hydrogen peroxide (in the SC-1 solution) is also addressed. © 2004 The Electrochemical Society. All rights reserved.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".