Sensitivity-Based Reliability Analysis of MEMS Acceleration Switch
Bibliographic record
Abstract
MEMS acceleration switches have been used in many engineering applications. In this paper, the reliability of MEMS switch is evaluated under various uncertainties from materials and manufacturing process. First, the performance of MEMS switch is modeled using 1D mass-spring-damper-contact system. Different from conventional lumped element methods, the model includes the effect of geometric parameters as well as contact conditions. The parameters of 1D models are calibrated using a high-fidelity finite element model. For reliability assessment, four different methods are used in order to compare computational cost as well as accuracy in predicting reliability. It turned out that sensitivity-based reliability method has several benefits, such as computational time and identifying important parameters that contribute significantly to reliability. The sensitivity analysis showed that the cross-sectional height and the length of folded-beam contribute most significantly to the reliability of switch-on condition. After changing the length of the beam, the probability of failure was improved from 0.168 to 0.0003.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".