Neural network control of a MEMS torsion micro mirror
Bibliographic record
Abstract
High-performance optical communication networks require stable switches to rapidly redirect a light beam from one port, or fiber, to another without converting the optical signal into an electrical signal. Micro-electromechanical (MEMS) optical switches are miniature devices that use tiny mirrors to alter the path of light. These switches enable all-optical capability, are small and compact, and cheap to fabricate. However, many torsion mirror optical switches are activated by electrostatic actuators that exhibit a pull-in phenomena which greatly effects system stability. The pull-in phenomena occurs when the gap between the electrodes in the actuator is reduced to less than two thirds of the original value thereby causing an uncontrolled contraction between the two sides the capacitor. This paper describes how a backpropagation neural network can be used to control an electrostatically actuated optical switch without using stiff suspension systems or mechanical stops. The two-layer network is applied to both single input and dual input MEMS torsion mirror optical switches. Simulation studies are presented to demonstrate how the proposed scheme allows the switching mechanism to operate in a stable range and avoid the effect of pull-in phenomena
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".