Design and characterization of a micromachined Fabry–Perot vibration sensor for high-temperature applications
Bibliographic record
Abstract
We have designed and characterized a MEMS-based Fabry–Perot device (MFPD) to measure vibration at high temperatures. The MFPD consists of a micromachined cavity formed between a substrate and a top thin film structure in the form of a cantilever beam. When affixed to a vibrating surface, the amplitude and frequency of vibration are determined by illuminating the MFPD top mirror with a monochromatic light source and analyzing the back-reflected light to determine the deflection of the beam with respect to the substrate. Given the device geometry, a mechanical transfer function is calculated to permit the substrate motion to be determined from the relative motion of the beam with respect to the substrate. Because the thin film cantilever beam and the substrate are approximately parallel, this two-mirror cavity arrangement does not require alignment or sophisticated stabilization techniques. The uncooled high-temperature operational capability of the MFPD provides a viable low-cost alternative to sensors that require environmentally controlled packages to operate at high temperature. The small size of the MFPD (85–175 µm) and the choice of materials in which it can be manufactured (silicon nitride and silicon carbide) make it ideal for high-temperature applications. Relative displacements in the sub-nanometer range have been measured and close agreement was found between the measured sensor frequency response and the theoretical predictions based on analytical 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".