Dynamics of large oscillations in electrostatic MEMS
Bibliographic record
Abstract
We present a comprehensive experimental study of the dynamics of electrostatic MEMS resonators under large excitations. We identified three frequency ranges where large oscillations occur; a non-resonant region driven by fast–slow dynamic interactions and two resonant regions. In these regions, we found a plethora of dynamic phenomena including cascades of period-doubling bifurcations, a bubble structure, homoclinic and cyclic-fold bifurcations, hysteresis, intermittencies, quasiperiodicity, chaotic attractors, odd-periodic windows within those attractors, Shilnikov orbits, and Shilnikov chaos. We encountered these complex nonlinear dynamics phenomena under relatively high dissipation levels, the quality factors of the resonators examined in this study were Q = 6.2 and 2.1. In the case of MEMS with higher quality factors ( Q > 100 ) , it is quite reasonable to expect those phenomena to appear under relatively low excitation levels (compared to the static pull-in voltage). This calls for a new paradigm in the design of electrostatic MEMS that seeks to manage dynamic phenomena rather than attempt to avoid them and, thereby, overly restricting the design space. We believe this is feasible given the repeatable and predictable nature of those phenomena.
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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 | 0.000 |
| 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) | 0.001 | 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".