Modal Interactions in Resonant Microscanners
Bibliographic record
Abstract
The fast response of micromirrors and their ability to achieve large scanning angles and low wavelength sensitivity, has made them an appealing substitute for traditional scanning and display technologies. To achieve large rotation angles, while minimizing the voltage requirements, the microscanner is excited at its resonance frequency and then used to steer a light beam along a surface. In this work, we develop a comprehensive model of a torsional microscanner. Based on the eigenvalue problem, we reduce the model to a 2-DOF lumped-mass model that captures the significant dynamics of the microscanner. We use the method of multiple scales to derive an approximate analytical solution of the microscanner response to combined DC and resonant AC voltage excitation. We examined the characteristics of the solution and found that, for a range of DC voltage, a two-to-one internal resonance occurs between the first two modes. Therefore, the energy fed to the first (torsional) mode may be channeled to the second (bending) mode causing an undesirable steady-state response. This phenomenon results in significant degradation in the microscanner performance, therefore, the designer needs to identify it, design around it, or control it.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".