Micro-assembly of three-dimensional rotary MEMS mirrors
Bibliographic record
Abstract
We present a novel approach to construct three-dimensional rotary micro-mirrors, which are fundamental components to build 1×N or N×M optical switching systems. A rotary micro-mirror consists of two microparts: a rotary micro-motor and a micro-mirror. Both of the two microparts are fabricated with PolyMUMPs, a surface micromachining process. A sequential robotic microassembly process is developed to join the two microparts together to construct a threedimensional device. In order to achieve high positioning accuracy and a strong mechanical connection, the micro-mirror is joined to the micro-motor using an adhesive mechanical fastener. The mechanical fastener has self-alignment ability and provides a temporary joint between the two microparts. The adhesive bonding can create a strong permanent connection, which does not require extra supporting plates for the micro-mirror. A hybrid manipulation strategy, which includes pick-and-place and pushing-based manipulations, is utilized to manipulation the micro-mirror. The pick-andplace manipulation has the ability to globally position the micro-mirror in six degrees of freedom. The pushing-based manipulation can achieve high positioning accuracy. This microassembly approach has great flexibility and high accuracy; furthermore, it does not require extra supporting plates, which greatly simplifies the assembly process.
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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 | 0.001 |
| 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".