Machine-learning-driven optimization of vertical fiber-to-chip coupling system for co-packaged optics and in-package optical I/O applications
Bibliographic record
Abstract
Efficient light coupling in photonic integrated circuits (PICs) is vital for co-packaged optics (CPO) and in-package optical I/O (OIO). Grating couplers in PICs offer a viable solution for vertical coupling to a fiber attachment above the chip but are sensitive to fabrication and alignment variations. Using micro-optics, like collimating micro-lenses, can mitigate these issues but adds system complexity. Designing such systems necessitates simulating light propagation across different scales, from the sub-wavelength feature size of the grating coupler to the millimeter scale of the micro-optics and fiber attachment above. We propose an automated optimization workflow driven by state-of-the-art machine learning algorithms that combines optical simulation techniques suitable for these scales. This workflow also includes robustness analysis to capture variations from fiber assembly, fabrication, and material errors, providing insights into key variations affecting yield in the manufacture 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".