Implementation of Traveling Wave Models of Grating-Based Integrated Optical Devices for Circuit Simulation
Bibliographic record
Abstract
This paper presents the development and use of a traveling wave model of waveguide-based grating devices for use as a compact model in a circuit-level simulator. Both passive and active devices are modeled, with the grating being characterized by coupling coefficients for the two counter-propagating waves. It is shown how the implicit carrier frequency can be offset from the Bragg frequency using two possible methods: either by the static detuning of the model or introducing a phase modulation into the coupling coefficients. Other physical aspects of the model are addressed such as dispersion and energy conservation. A comparison to a 1D Yee-cell model is used to verify the applicability of the traveling wave model. As an example of the circuit simulation of a passive device, an optical code generating application is used and it is noted that for a passive device the interface between the compact model and the circuit simulator is not a concern. Using traveling-wave-based laser simulations of grating-based laser structures it is demonstrated that the model captures the complex behaviour of the devices. In particular the lasing frequency is naturally produced from the model and the introduction of delay elements into the structure can be used to restrict the laser to single mode operation. A number of examples are used to illustrate important aspects of its use as a compact model. Firstly, it is shown that an operating point for either of the laser configurations can be constructed and detuning used to produce an un-modulated output, allowing for much more efficient simulations. A final example uses a directly modulated laser to illustrate the effect of back reflection on the stability of the laser simulation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".