Two-axis MEMS positioner for waveguide alignment in silicon nitride photonic integrated circuits
Bibliographic record
Abstract
Alignment is critical for efficient integration of photonic integrated circuits (PICs), and microelectromechanical systems (MEMS) actuators have shown potential to tackle this issue. In this work, we report MEMS positioning actuators designed with the ultimate goal of aligning silicon nitride (SiN) waveguides either to different outputs within a SiN chip or to active chips, such as lasers and semiconductor optical amplifiers. For the proof-of-concept, suspended SiN waveguides implemented on a silicon-on-insulator wafer were displaced horizontally in the direction of light propagation to close an initial gap of 6.92 µm and couple the light to fixed output waveguides located on a static section of the chip. With the gap closed, the suspended waveguides showed ∼ 345 nm out-of-plane misalignment with respect to the fixed waveguides. The suspended waveguides can be displaced laterally by more than ±2 µm. When the waveguides are aligned and the gap closed, an average loss of -1.6 ± 0.06 dB was achieved, whereas when the gap is closed with a ± 2 µm lateral displacement, a maximum average loss of ∼ -19.00 ± 0.62 dB was obtained. The performance of this positioner does not only pave the way for active chip alignment, but it could also be considered for optical switching applications.
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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 | 0.000 |
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".