Tailoring the microstructure and surface morphology of metal thin films for nano-electro-mechanical systems applications
Bibliographic record
Abstract
Metallic structural components for micro-electro-mechanical/nano-electro-mechanical systems (MEMS/NEMS) are promising alternatives to silicon-based materials since they are electrically conductive, optically reflective and ductile. Polycrystalline mono-metallic films typically exhibit low strength and hardness, high surface roughness, and significant residual stress, making them unusable for NEMS. In this study we demonstrate how to overcome these limitations by co-sputtering Ni-Mo. Detailed investigation of the Ni-Mo system using transmission electron microscopy and high-resolution transmission electron microscopy (TEM/HRTEM), x-ray diffraction (XRD), nanoindentation, and atomic force microscopy (AFM) reveals the presence of an amorphous-nanocrystalline microstructure which exhibits enhanced hardness, metallic conductivity, and sub-nanometer root mean square (RMS) roughness. Uncurled NEMS cantilevers with MHz resonant frequencies and quality factors ranging from 200-900 are fabricated from amorphous Ni-Mo. Using a sub-regular solution model it is shown that the electrical conductivity of Ni-Mo is in excellent agreement with Bhatia's structural model of electrical resistivity in binary alloys. Using a Langevin-type stochastic rate equation the structural evolution of amorphous Ni-Mo is modeled; it is shown that the growth instability due to the competing processes of surface diffusion and self-shadowing is heavily damped out due to the high thermal energies of sputtering, resulting in extremely smooth films.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".