Bibliographic record
Abstract
This paper presents micro-electro-mechanical-systems (MEMS) optical elements with high angular deflection arranged in arrays to perform dynamic laser beam focusing and scanning. Each element selectively addresses a portion of the laser beam. These devices are useful in medical and research applications including laser-scanning microscopy, confocal microscopes, and laser capture micro-dissection. Such laser-based imaging and diagnostic instruments involve complex laser beam manipulations. These often require compound lenses and mirrors that introduce misalignment, attenuation, distortion and light scatter. Instead of using expensive spherical and aspherical lenses and/or mirrors for sophisticated laser beam manipulations, we propose scalable adaptive micro-opto-electro-mechanical-systems (MOEMS) arrays to recapture optical performance and compensate for aberrations, distortions and imperfections introduced by inexpensive optics. A high-density array of small, individually addressable, MOEMS elements is similar to a Fresnel mirror. A scalable 2D array of micro-mirrors approximates spherical or arbitrary surface mirrors of different apertures. A proof of concept prototype was built using PolyMUMPTM due to its reliability, low cost and limited post processing requirements. Low-density arrays (2x2 arrays of square elements, 250x250μm each) were designed, fabricated, and tested. Electrostatic comb fingers actuate the edges of the square mirrors with a low actuation voltage of 20 V - 50 V. CoventorWareTM was used for the design, 3D modeling and motion simulations. Initial results are encouraging. The array is adaptive, configurable and scalable with low actuation voltage and a large tuning range. Individual element addressability would allow versatile uses. Future research will increase deflection angles and maximize reflective area.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".