A Nonlinear Pulse Shaping Method Using Resonant Piezoelectric MEMS Devices
Bibliographic record
Abstract
In this article, a methodology for increasing the displacement of the membrane in nonlinear transducers is presented. This methodology that relies on pulse shaping is based on the frequency modulation of the excitation signal which in turn results in an amplitude modulation of the displacement of the resonator. The benefits of pulse shaping include the increase of the displacement of the membrane of the resonator, the ability to leverage two mechanisms to dynamically tune the resonant frequency of the device and a relative control of the decay time of the resonator. These properties have been verified using simulations and experimental results. The experimental results are performed using two nonlinear resonators with a frequency of 3.9 and 7.9 kHz. With a constant amplitude of the excitation voltage, experimental results show that the use of pulse shaping allows a velocity increase of the membrane of a piezoelectric microelectromechanical systems (MEMS) resonator of up to 191% for a softening type resonator (STR), and 348% for a hardening type resonator (HTR). The frequency tuning mechanism allowed the operation of the STR and of the HTR over a bandwidth of 280 and 115 Hz, respectively, while providing higher velocity than with the non-optimized excitation signal. The resulting pulse shaping methodology can be applied to other nonlinear resonators as shown using simulation and experimental results. Therefore, this work should lead to an increase of the use of nonlinear resonators for various applications.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".