Novel approach for microassembly of three-dimensional rotary MOEMS mirrors
Bibliographic record
Abstract
We present a novel approach to construct 3-D 1×N rotary micromirrors, which are fundamental components in optical switching systems. A rotary micromirror consists of two microparts: a rotary micromotor and a micromirror. Both of the two microparts are fabricated with PolyMUMPs,TM (MEMSCAP, Research Triangle Park, North Carolina), a surface micromachining process. A sequential robotic microassembly process is developed to join the two microparts together to construct the 3-D device. To achieve high positioning accuracy and strong mechanical connection, the micromirror is joined to the micromotor using an adhesive mechanical fastener. The mechanical microjoint has self-alignment capability and provides a temporary joint between the two microparts. The adhesive bonding creates a strong permanent connection between the two microparts. The adhesive mechanical fastener does not require extra supporting plates to fix the micromirror, which simplifies the microassembly process and makes it possible to automatically assemble the rotary micromirror. A hybrid manipulation strategy, which includes pick-and-place and pushing-based micromanipulations, is utilized to assemble the micromirror onto the micromotor. The pick-and-place manipulation has the ability to globally position the micromirror with multiple degrees of freedom. The pushing-based manipulation can achieve high positioning accuracy. This novel approach provides great flexibility and high accuracy for assembling the complex micromirror.
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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 | 0.001 |
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".