Hybrid MEMS Actuator With 3 Degrees-of- Freedom for Efficient Planar Optical Switching
Bibliographic record
Abstract
This work demonstrates a hybrid MEMS actuator platform that combines electrostatic and piezoelectric actuators to displace a suspended MEMS platform along 3 degrees-of-freedom (DOF). The prototype MEMS actuator tested produces maximum displacements of$8.8 \mu \text{m}$along the positive X-axis, and$8.5 \mu \text{m}$along the negative X-axis, with digital control. Analog control produces maximum displacements of$5.9 \mu \text{m}$along the positive X-axis, and$5.8 \mu \text{m}$along the negative X-axis. It can also provide$2.2 \mu \text{m}$of in-plane displacement along the negative Y-axis with analog control. The electrostatic actuator used for displacement along the negative Y-axis also compensates for any out-of-plane misalignment between the suspended MEMS platform and fixed silicon due to the significant residual stress caused by deposited piezoelectric material layers. In addition, up to 550 nm of misalignment compensation using the electrostatic actuator before electrostatic pull-in is demonstrated. The piezoelectric actuator provides fine alignment capability with a maximum out-of-plane displacement of 200 nm and 100 nm with analog control along the positive and negative Z-axis, respectively. This hybrid MEMS actuator can be integrated with channel waveguides for efficient planar optical switching applications. [2023-0121]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".