Fabrication of out-of-plane micromirrors in silicon-on-insulator planar waveguides
Bibliographic record
Abstract
Emerging optical waveguide spectrometers, sensors, and biochips require the simultaneous acquisition of numerous optical signals. Aligning many tens or hundreds of optical fibers to waveguides is a formidable and costly exercise in packaging. We propose a data acquisition scheme using arrays of micromirrors to redirect light propagating in each waveguide out of the device surface, where all the beams can be collected by a single imaging array. Micromirrors, which are the key elements in this data acquisition scheme, were fabricated in 2μm thick silicon-on-insulator waveguides. Chemically assisted ion beam etching (CAIBE) was used to obtain inclined mirror facets, benefiting from the unique capability of CAIBE to tilt the sample relative to the ion beam. Finite difference time domain (FDTD) simulations of an ideal micromirror oriented at 45° to the propagation direction predict a >96% out-coupling efficiency (loss of 0.14dB) and a negligible polarization dependent loss (PDL). The measured total insertion loss for the fabricated waveguide and mirror was −5dB, including fiber-to-waveguide coupling loss, waveguide propagation loss, and mirror loss due to fabrication errors and diffraction, with a PDL of 0.7dB. The dependence of micromirror performance on facet angle and etch depth was studied by FDTD simulations. The influence of CAIBE etching chemistry and selectivity in obtaining optimum mirror parameters is discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".