3D Laser Structured Mirror-Waveguide Circuits: a New Optical PCB Platform for Silicon Photonics
Bibliographic record
Abstract
The demands for faster networks and growing requirements in data centers have spurred the development of photonic integrated circuits [1] , particularly in designing a universal platform for optical/electrical interconnects. Some headway towards an optical PCB platform has been made, for instance, chip-to-chip polymer waveguide interconnects [2] , ion-exchange waveguide flip-chip packaging [3] , and laser-written 3D circuits in glass [4] . However, the design of an all-optical platform for chip-to-chip or chip-to-network (i.e. chip-to-fiber) communications still faces challenges, namely densification of I/O channels and bending radius constraints for optical waveguide routing. The latter can be addressed by using total internal reflection (TIR) surfaces to bend light at sharp angles, such as for compact fiber coupling to Silicon Photonics (SiP) gratings with a polished glass wedge or angled SMF array [5] . In this regard, we propose a platform which utilizes femtosecond laser assisted chemical etching to embed TIR micro-mirrors and facilitate sharp bending of optical waveguide circuits inside of fused silica glass. Full 3D optical routing is proposed with multi-level waveguides and high channel-density.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".