Electrodeposition of multilayer cantilevers for MEMS applications
Bibliographic record
Abstract
An automated electrodeposition process has been developed to produce low-stress freestanding metallic cantilevers for rf MEMS applications. The cantilevers are made of multiple layers of three different materials to achieve stress compensations. The fabrication is carried out using a PC-controlled deposition system developed in house. We found that the deposition rate increases with the increase of applied voltages and the thickness of the films has a linear relationship with the deposition time for individual layers. The deposited multilayer films have a smooth surface and are uniform in thickness. The morphology of the films is independent on the thickness of the multilayer films when a moderate deposition rate is adopted. Cantilevers with a single sandwich structure are fabricated and studied. All of the samples have demonstrated freestanding cantilevers with a uniform thickness and smooth surfaces. However, a slightly up bending (or sloping) along the cantilever is observed on all cantilevers regardless the big difference in thickness (ranging from 1.5 μm to 5 μm), which indicates the presence of some uncompensated stress. Cantilevers with a double sandwich structure (with two strengthening material) are also fabricated to further reduce the stress. The relative thickness of the individual layers for the three materials is found to govern the stress compensation process and to control the quality of the cantilevers. The strengthening layer not only plays an active role in stress compensation, it also hardens the multilayer films and to cut down the required minimum thickness for the cantilevers. The optimized deposition conditions produce essentially flat cantilevers with a length up to 800 μm. For each sample, the two cantilevers are found to be identical in shape, thickness and morphology.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".