Scalable Switched Slab Coupler Based Optical Phased Array on Silicon Nitride
Bibliographic record
Abstract
A two-dimensional optical-phased array is demonstrated by using a multiple-input star coupler, compatible with FMCW LiDAR. Previous approach using a single-input design achieves two-dimensional beam-steering by relying on a tunable laser source, taking advantage of grating coupler radiation angle wavelength dependance and variation of the waveguide refractive index. While implementing a convenient way to distribute power in a single step, star coupler architecture is inefficient in terms of employed waveguide length and thus, optical loss and footprint. Multi-input approach partially alleviates this by condensing several single-input devices into one, permitting to reduce the footprint proportionally to the employed number of inputs. We fabricated in silicon nitride technology a proof of concept steerer with beam waist 0.36$^\circ$×0.175$^\circ$addressing a field of view of 15$^\circ$×2.8$^\circ$. A new design iteration is also reported with and 0.24$^\circ$×0.16$^\circ$beam waist and 15$^\circ$×11.2$^\circ$field of view. Implications of this optical-phased array chips from a LiDAR system perspective are also presented.
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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".