Pull-in voltage study of electrostatically actuated fixed-fixed beams using a VLSI on-chip interconnect capacitance model
Bibliographic record
Abstract
A highly accurate computationally efficient closed-form model has been developed to determine the pull-in voltage of an electrostatically actuated fixed-fixed beam. The approach includes the electrostatic spring softening effects due to the fringing field capacitances along with the nonlinear spring hardening effects associated with the load-deflection characteristics of a uniformly loaded fixed-fixed beam. Meijs and Fokkema's highly accurate empirical formula for the capacitance of a VLSI on-chip interconnect has been used to determine the spring softening effects due to the fringing field capacitances. The developed model has been verified by comparing the results with published experimentally verified three-dimensional (3-D) finite element analysis (FEA) results and with those from other published representative closed-form models. The developed model can determine the pull-in voltage with a maximum deviation of 1.27% from the FEA results for small deflections and for large deflections (airgap-beam thickness ratio =12), the deviation from the FEA results is 2.0%. A maximum deviation of 0.5% from the FEA results has been observed for extreme fringing field cases (beamwidth-airgap ratio /spl les/0.5). The model's accuracy range is better compared to the other published models.
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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 | 0.001 | 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".