Design and fabrication of capacitive micromachined optical focusing deformable MEMS mirrors for ultrafast tunable focusing (Conference Presentation)
Bibliographic record
Abstract
We introduce Capacitive Micromachined Optical Focusing (CMOF) MEMS consisting of a miniature circular mirrored-membrane which can be electrostatically actuated to change mirror curvature. The central deflection zone is a close approximation to a parabolic mirror. The device is fabricated with minimal membrane mass to be only slightly larger than a diffraction-limited focus of a Gaussian beam. This device is a good candidate for fast tuning of the radius of curvature of laser beams at greater than MHz tuning rates, a feat difficult to achieve with current technologies. We present the design, modeling, and fabrication of a high-speed focusing CMOF MEMS platform. We have developed an equivalent circuit model for CMOFs which is capable of full nonlinear analyze of the CMOFs, and it is validated by ANSYS finite element method (FEM) simulations. By using the equivalent circuit model the non-linear transient response of a CMOF can be rapidly obtained and controlled with nonlinear control systems. The first generation of the proposed device is fabricated with a sacrificial-release process. Fabricated CMOFs have a silicon nitride membrane with 32um radius and 300nm gap spacing between top and bottom electrode. These CMOFs can effectively change their focal length from infinity to 3mm. We tested the device in an optical lens-assembly, and recorded 1mm focal point change by only 100nm deflection on the CMOF surface as measured using a Shack-Hartman wavefront sensor.
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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 | 0.000 |
| 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".