Optical MEMS: From Micromirrors to Complex Systems
Bibliographic record
Abstract
Microelectromechanical system (MEMS) technology, and surface micromachining in particular, have led to the development of miniaturized optical devices with a substantial impact in a large number of application areas. The reason is the unique MEMS characteristics that are advantageous in fabrication, systems integration, and operation of micro-optical systems. The precision mechanics of MEMS, microfabrication techniques, and optical functionality all make possible a wide variety of movable and tunable mirrors, lenses, filters, and other optical structures. In these systems, electrostatic, magnetic, thermal, and pneumatic actuators provide mechanical precision and control. The large number of electromagnetic modes that can be accommodated by beam-steering micromirrors and diffractive optical MEMS, combined with the precision of these types of elements, is utilized in fiber-optical switches and filters, including dispersion compensators. The potential to integrate optics with electronics and mechanics is a great advantage in biomedical instrumentation, where the integration of miniaturized optical detection systems with microfluidics enables smaller, faster, more-functional, and cheaper systems. The precise dimensions and alignment of MEMS devices, combined with the mechanical stability that comes with miniaturization, make optical MEMS sensors well suited to a variety of challenging measurements. Micro-optical systems also benefit from the addition of nanostructures to the MEMS toolbox. Photonic crystals and microcavities, which represent the ultimate in miniaturized optical components, enable further scaling of optical MEMS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".