Bibliographic record
Abstract
In recent years, the optical application of MEMS (MOEMS, micro-opto-electro-mechanical-system) is widely studied, because of its intrinsic benefits [P. Rai-Choudhury, 2000][Hiroyuki Fujita et al., 2000]. One of most striking development is MOEMS sensors. The feature size and motion of MEMS devices are in the range of sub-micrometer, which make MEMS an effective mechanism to realize optical interference, hence a sensor system can be achieved by monitoring these micro-interferometers. This work reports a whole process of modeling, fabrication, and measurement of an optical MEMS sensor system. The sensor tip is designed as a Fabry-Perot cavity etched on the silicon chip, light is incident from the optical fiber, and simultaneously the reflected optical signal from the cavity is monitored. When certain ambient change forced on the tip, the membrane of the cavity vibrates deviating from resonance; consequently the reflected light is changed by means of central wavelength shift, and power degradation. CMOS modeling tools are used, to design the tip prototype and the fabrication process. Single mode fiber links between the tip and source/detector, the light source is 1550 nm broadband. Both OTDR and OSA are used to precisely decode the magnitude of ambient change. Next, the analogous sensor tips are reproduced and arrayed to realize a sensor network, thus a momentary impact will be positioned as well as quantified through spatial division multiplex [K.T.V Grattan et al., 2000]. Upon the performances, MOEMS sensor is compared with other possible sensing mechanisms, with regard to the physical features, sensing results, fabrication easiness, and application limits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".