Tuning the dynamic behaviour of cantilever MEMS based sensors and actuators
Bibliographic record
Abstract
Purpose This paper seeks to establish an analytical reference model in order to optimize the frequency response of MEMS cantilever structures using cutouts. Design/methodology/approach Presented in this work is a method to tune the frequency response of MEMS cantilevers by using single cutouts of various sizes. From an interpretation of the analytical results, mass and stiffness domains are defined as a function of the cutout position on the cantilever. In this regard, the elastic properties of the MEMS cantilever can be trimmed through mechanical tuning by a single cutout incorporated into the device geometry. The Rayleigh‐Ritz energy method is used for the modeling. Analytical results are compared with FEM and experimental results. Findings The eigenvalues are dependent on the position and size of the cutout. Hence, the frequency response of the cantilever can be tuned and optimized through this approach. Research limitations/implications MEMS microsystems are sensitive to microfabrication limitations especially at the boundary support of suspended structures such as microcantilevers. Practical implications MEMS cantilevers are resistant to low level vibrations due to their low inertia and the elastic properties of the silicon material. For sensor applications these qualities are highly regarded and explored. This analysis will contribute to the performance optimization of atomic force microscope (AFM) probes and micromechanical resonators. Originality/value A method to tune, with cutouts, the frequency response of microcantilevers is proposed. The data can provide insight into the performance optimization of micromechanical resonators through mass reduction. For industrial applications requiring optimized responses the cutouts can be incorporated into microcantilevers through focused ion beam (FIB) machining or laser drilling, for example.
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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".