Engineering Ni-Silicide Nanocontacts for 3D Silicon Devices via Geometrical Confinement Control
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Nanoscale Ni-silicide alloys are critical components for future generations of 3D electronic devices based on active Si nanostructures, with applications in nanoelectronics, energy conversion, and sensing. This study investigates how geometrical confinement in such nanostructures influences diffusion-driven silicidation, ultimately determining the alloy formation sequence, phase composition, and volumetric expansion. The silicidation of controlled Ni volumes is investigated on vertical silicon nanowires (NW) and nanosheets (NS) under various annealing conditions. The silicide phases and interface morphologies are characterized using high-resolution (scanning) transmission electron microscopy (HR-TEM, HR-STEM), energy-dispersive X-ray spectroscopy (EDX), and four-dimensional scanning transmission electron microscopy (4D-STEM) for nanoscale Ni–Si phase mapping. Under conditions of strong geometric confinement, NiSi 2 is observed to form with faceted, prism-like morphologies aligned with Si (111) planes, features not typically present in planar or bulk samples. This anisotropic growth is associated with preferential Ni diffusion along nanostructure surfaces and limited Si counter-diffusion through the silicide. The resulting NiSi 2 interfaces are structurally distinct and may contribute to reduced contact resistance in both p-type and n-type silicon nanostructures, supporting their integration in 3D device architectures.
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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) | 0.001 | 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".