Modeling of Wet Stiction in Microelectromechanical Systems (MEMS)
Bibliographic record
Abstract
Stiction, which is a term commonly used in microelectromechanical systems (MEMS) to refer to adhesion, is a major failure mode in MEMS. Undesirable stiction, which results from the contact between surfaces, can severely compromise the reliability of MEMS. In this paper, a model is developed for predicting stiction between uncharged micro parts interacting in a humid environment. In this condition, for hydrophilic surfaces, the capillary and asperity deformation forces are dominant. Here, using a newly developed multiple asperity contact model, a model is developed for the capillary force between rough micro surfaces, and the new model is combined with a newly developed elastic/plastic deformation model for rough surfaces to solve for the equilibrium of the forces. This in turn yields the equilibrium distance between micro surfaces using which the apparent work of adhesion can be found. The theoretical results are compared with the available experimental data from literature. The developed model can be easily used for design purposes. If the topographic data and material constants are known, the desirable adhesion parameters can be quickly found from the model.$\hfill$[2006-0274]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".