Bibliographic record
Abstract
Planar Photonic Circuits can perform many useful functions in optical communications systems, such as wavelength division multilplexing (WDM), optical channel add/drop, fibre/waveguide coupling, and amplifier gain equalization. They perform these functions by the interaction of the device structure with the light inside them. There are very effective and proven numerical methods available for modelling this interaction, such as the Beam Propagation Method (BPM), the Finite Difference Time Domain (FDTD) method, and coupled mode theory (CMT). However, these methods work on a microscopic level (typically the smallest distance is about 0.1 microns), but photonic circuits, on the other hand, can occupy an entire wafer (scale: 10 cm). The analysis must span 5 or more orders of magnitude in the change in scale. The successful analysis needs to combine the basic microscopic techniques with an approach at a more abstract, or system, level. It is interesting that software designed for the analysis of optical communication systems can be applied to planar photonic circuits. This paper shows an example of a practical photonic circuit, a lattice filter, that cannot be analysed by BPM alone. It will be demonstrated that when used with a system level analysis, the whole device can be simulated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".