Uncertainty Quantification for Cantilever Based MEMS Switches Considering Bouncing Dynamics
Bibliographic record
Abstract
Design methods of MEMS switches are typically based on deterministic approaches, where the parameters such as geometrical and physical properties as well as the operating conditions that characterize the behavior of systems are assumed to be known precisely. However, in practice, due to the batch-production processes used in MEMS fabrication as well as the micron-scale dimensions of the structural elements, consideration of uncertainties in system parameters and an understanding of their effects are warranted and should be investigated in order to improve the switch performance and reliability. The primary purpose of the present paper is to perform uncertainty quantification predictions for MEMS switches based on the transient dynamic response, in particular, the bouncing behavior. A suitable mathematical model that captures the bouncing dynamics and previously validated via experiments is employed for this purpose. In particular, quantification of performance in terms of second order statistics is performed to predict propagation of uncertainties in Young’s modulus, beam width, beam thickness as well as actuation voltage. The influence of these uncertainties on significant switch performance parameters such as initial bounce time as well as maximum bounce height have been quantified.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".