Experimental investigation on the dynamics of MEMS structures
Bibliographic record
Abstract
Modeling, manipulating and testing of the dynamic performances of micro-electro-mechanical systems (MEMS) devices are very important in building successful microsystems. However, MEMS devices pose several significant difficulties in characterization. The physical dimensions of MEMS devices are such that conventional measurement and characterization techniques cannot be used since the sensor would interfere with the measurement. Hence, non-contact sensing systems offer many advantages for MEMS characterization. One important issue in characterizing and troubleshooting MEMS devices is the differentiation between electrical and mechanical effects. By definition, MEMS devices are comprised of electrical and mechanical components forming integrated electro-mechanical systems. The dynamic response of these devices is often difficult to determine because of the coupled electro-mechanical behavior. It is also known that the dynamic response is influenced by the limitation of fabrication processes and the material conditions. Hence, this paper proposes a simpler method to verify the dynamic behavior of MEMS structures using Laser Doppler Velocimeter (LDV). Non-contact vibration measurements are thus possible with such a testing system that can lead to significant improvements in the accuracy and precision of MEMS testing. The dynamic experiments are conducted on different devices and the test results are compared with prediction.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".