Uncertainty Considerations for Nonlinear Dynamics of a Class of MEMS Switches Undergoing Tip Contact Bouncing
Bibliographic record
Abstract
Batch fabrication processes used to produce micro-electro-mechanical systems (MEMS) are prone to uncertainties in the system geometrical and contact parameters as well as material properties. However, since the common design method for these systems is typically based on precise deterministic assumptions, it is necessary to get more insight into their variations. To this end, understanding the influences of uncertainties accompanied by these processes on the system performance and reliability is warranted. The present paper focuses on predictions of uncertainty measures for MEMS switches based on the transient dynamic response, in particular, the bouncing behavior. To understand and quantify the influence of pertinent parameters on the bouncing effects, suitable mathematical model that captures the bouncing dynamics as well as the forces that are dominant at this micron scale are employed. Measure of performance in terms of second-order statistics is performed, particularly for the beam as well as beam tip parameters since excessive tip bounce is known to degrade switch performance. Thus, the present study focusses on the influence of uncertainties in the beam tip geometry parameters such as beam tip length/width as well as contact asperity variables such as the area asperity density and the radius of asperities. In addition to beam tip parameters, this study quantifies the effects of uncertainties in Young's modulus, beam thickness as well as actuation voltage. These influences on significant switch performance parameters such as initial contact time and maximum bounce height have been quantified in the presence of interactive system nonlinearities.
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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".