State-of-the-art and next-generation integrated photonic design
Bibliographic record
Abstract
The relentless need for higher bandwidth, lower power and lower cost data communications has driven tremendous innovation in integrated photonics in recent years. This innovation has been supported by state-of-the-art electronic-photonic design automation (EPDA) workflows, which enable process design kit (PDK) centred schematic driven design and layout, as well as statistically enabled electro-optical simulation. In addition, custom components can be introduced and optimized for a specific foundry process using advanced methods such as photonic inverse design and machine learning. While much of the innovation has been motivated by data communications, it has enabled a variety of different applications such as sensing, integrated LiDAR and quantum information technologies. We discuss the latest innovations in EPDA workflows and show how a silicon photonic ring-based wavelength demultiplexing (WDM) system can be easily designed, simulated and implemented. In addition, we discuss the extension of these workflows to support the design and simulation of quantum photonic devices, enabling designers to consider the effects of realistic sources and manufacturing imperfections when designing quantum building blocks to meet specific fidelity and fault tolerance thresholds.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".