A Two-Row Interdigitating-Finger Repulsive-Torque Electrostatic Actuator and Its Application to Micromirror Vector Display
Bibliographic record
Abstract
This paper presents a novel two-row interdigitating-finger repulsive-torque electrostatic actuator and its application to the micromirror vector display. The actuator consists of upper and lower rotation units and an output beam. Each unit has one row of rotation fingers, i.e., the lower and upper rotation fingers, which interdigitate with each other. The lower and upper rotation fingers are subject to different electric potentials. Each rotation finger and its underneath aligned fixed finger are subject to the same electric potential. The actuator generates repulsive torques to rotate the lower and upper fingers out-of-plane with the maximum rotation not limited either by the gap between fingers and the substrate nor by the pull-in effect. The output beam is connected to the lower row of rotation fingers, and therefore, the actuator has the same rotation as the lower unit. The actuator requires only two thin films and is suitable for surface micromachining. The model is established to verify the actuator and calculate its performance. Prototypes are fabricated and tested. The experimental results show that the actuator achieved a mechanical rotation of 11.5° at the static driving voltage of 150 V and a resonant frequency of 380 Hz with a sinusoidal driving voltage varying from 20 to 70 V. This paper demonstrates the application of the actuator in micromirror vector display. Four actuators are connected to the peripheries of a micromirror plate and independently controlled to drive the mirror plate to achieve quasi-static 2-D rotation. A laser beam is reflected and steered by the mirror plate to any desired direction to realize vector display. The prototype of the vector display micromirror is fabricated and tested. A vector display module integrating the micromirror, a laser, and a concave lens is built. Based on this module, an automotive head-up display system is developed, which is able to display the navigation map, speed, turning sign, and vehicle location on a translucent screen.
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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 | 0.000 | 0.000 |
| 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".