Effects of Contact Roughness and Trapped Free Space on Characteristics of RF-MEMS Capacitive Shunt Switches
Bibliographic record
Abstract
Dielectric surface roughness and top electrode metal asperities tend to affect both the lifetime reliability and the frequency response of radio frequency microelectromechanical systems (RF-MEMS) capacitive shunt switches. The downstate (OFF) capacitance of these switches is considerably affected by the interface irregularities and the free space trapped between the contacting surfaces of a MEMS switch. Attempts have been made to develop models to describe the effects of interface roughness and the trapped free space, yet no comprehensive model, including closed form analytical equations, still exists. A very large body of research has been conducted in physics to model the types of surface fluctuations and interface irregularities between two contacting media. Among these models, the self-affine fractals can be used to describe different growth and deposition techniques, such as sputtering, thermal evaporation, and molecular bean epitaxy. Based on this concept, in this paper, we developed a model that incorporates the effects of contact roughness and free space into classic equations describing the electromechanical behavior of a capacitive MEMS switch. The resulting closed form equations can properly predict the electrostatic and mechanical characteristics of a typical RF-MEMS switch. Measurement and test results from the reported works are used to confirm the validity of the model.
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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) | 0.000 | 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".