Bibliographic record
Abstract
This chapter describes the process of optoelectronic parameter extraction starting from the geometric parameters in components, followed by their use in circuit simulations. The techniques described can be used for components such as waveguides, couplers, Y-junctions, grating couplers, edge couplers, waveguide couplers, ring resonators, modulators, and filters and photo detectors, as described in Parts II and III. For fixed-cell designs, the components can be simulated once using the appropriate physical solvers and the optoelectronic parameters can be extracted for later use. For parameterized-cell designs, it is necessary to perform parameter sweeps over a range of possible geometries to create lookup tables or validated phenomenological models. We then show examples of how these extracted optoelectronic parameters can be used in photonic circuit simulations. Need for photonic circuit modelling Silicon photonics is a technology enabling large-scale integration of photonic components into photonic circuits. There is increasing interest in photonic integrated circuits in silicon systems for a variety of applications including on-chip and inter-chip communication systems. Broad adoption of silicon photonics technology for circuits and systems requires standardization in the design flow that is similar to what is available for electrical circuit design. This chapter describes methods for modelling optical circuits. The methods presented here are integrated as part of a design methodology that takes advantage of commercial electronic-design automation (EDA) tools for circuit design and layout, as described in Chapter 10. Numerical methods such as finite-difference time domain (FDTD) and eigen-mode solvers coupled with solutions to optoelectronic equations are workhorses in silicon photonic component-level design. These methods unfortunately do not scale well as the number of components in the photonic circuit increases. Thus, modelling approaches that are computationally efficient yet accurately represent complex nanophotonic devices need to be employed in circuit simulations. In addition, the physical layout can affect the circuit response and needs to be considered. These issues have been addressed by the CMOS electronics industry, and are beginning to be incorporated into silicon photonic design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".