Shaped combs and parametric amplification in inertial MEMS sensors
Bibliographic record
Abstract
In this paper slope-shaped comb are designed, modeled and experimentally verified for electrostatic parametric amplification of micro-displacements. Parametric resonance techniques applied to inertial sensors requires a periodic stiffness modulation, as expressed by the Mathieu equation, and is generally implemented using gap-varying and non-overlapping combs. MEMS vibratory gyroscopes provide the opportunity for applying parametric amplification for both the sensing and driving modes. While gap-varying combs are efficiently used for small displacements (e.g. the sensing mode), larger displacements require a different approach, where slope-shaped combs are a good alternative. Analytical model of slope shaped combs is carried out and compared with experimental characterization of fabricated devices. The analytical model predicts a spring modulation of 0.1%-0.65% spring modulation for 10 V to 40V applied common mode DC bias, and it can be increased for steeper slopes. The parametric amplification operation was experimentally tested using linear differential voltage actuation on the area-varying combs and a phase-synchronised common mode voltage (using a PXIe 1062Q DAQ controller) applied to the left and right shaped combs.
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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".