MEMS-based flexible reflective analog modulators (FRAM) for projection displays: a technology review and scale-down study
Bibliographic record
Abstract
A MEMS based technology for projection display is reviewed. This technology relies on mechanically flexible and reflective microbridges made of aluminum alloy. A linear array of such micromirrors is combined with illumination and Schlieren optics to produce a pixels line. Each microbridge in the array is individually controlled using electrostatic actuation to adjust the pixels intensities. Results of the simulation, fabrication and characterization of these microdevices are presented. Activation voltages below 250 V with response times below 10 μs were obtained for 25 μm × 25 μm micromirrors. With appropriate actuation voltage waveforms, response times of 5 μs and less are achievable. A damage threshold of the mirrors above 8 kW/cm 2 has been evaluated. Development of the technology has produced projector engines demonstrating this light modulation principle. The most recent of these engines is DVI compatible and displays VGA video streams at 60 Hz. Recently applications have emerged that impose more stringent requirements on the dimensions of the MEMS array and associated optical system. This triggered a scale down study to evaluate the minimum micromirror size achievable, the impact of this reduced size on the damage threshold and the achievable minimum size of the associated optical system. Preliminary results of this scale down study are reported. FRAM with active surface as small as 5 μm × 5 μm have been investigated. Simulations have shown that such micromirrors could be activated with 107 V to achieve f-number of 1.25. The damage threshold has been estimated for various FRAM sizes. Finally, design of a conceptual miniaturized projector based on 1000 × 1 array of 5 μm × 5 μm micromirrors is presented. The volume of this projector concept is about 12 cm 3 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".