Comparison of three finite element models for analysis of MEMS micromirrors
Bibliographic record
Abstract
We investigate finite element modeling of MEMS micromirrors actuated electrostatically by means of tools available in ANSYS finite element modeling software, and compare numerical results with analytical solutions for the static analysis of MEMS micromirrors. MEMS micromirrors must be accurately modeled in order to achieve precise optical positioning. Analysis of MEMS micromirrors leads to the study of structural and electrostatic fields. Finite Element (FE) method is an effective technique to model structural and electrostatic fields. The FE analysis of these coupled fields is accomplished by several tools in ANSYS. This paper models torsional and flexural-torsional micromirrors by different methods in ANSYS. These methods include: (a) a sequential coupled electrostatic and structural field tool; (b) a directly coupled electrostatic and structural field tool employing one-dimensional (1D) transducer element; and (c) a directly coupled electrostatic and structural field tool utilizing a 2-D or 3-D reduced order model. The torsional micromirror is of 1000 by 250 microns square, and the flexural-torsional micromirror is of 100 by 100 microns square. The numerical results are compared with analytical solutions. Comparisons show advantages and disadvantages of these tools for MEMS micromirror modeling. These comparisons allow a selection to be made of the most suitable tool for a given modeling task and assess the accuracy of analytical solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".