Integration of traveling wave laser model into an mixed electrical/optical circuit simulator
Bibliographic record
Abstract
Summary form only given. The integration of optical and electronic devices to achieve lower costs and higher functionality is attractive for a wide variety of opto-electronic applications. The key to innovation at the design level for these circuits is the availability of highly skilled personnel able to take advantage of sophisticated computer aided design (CAD) platforms. These tools enable them to undertake quick iterations of the design and simulation loop. The creation of such tools will need two interrelated initiatives: 1) The development of novel simulation and modelling techniques that will enable a robust and efficient simulation capability. These capabilities will include distributed thermal effects; modeling of non-linear optical devices; and the development of robust models for simulating circuits containing resonant devices. 2) The simulation and analysis of general, large and complex mixed electro-optic circuits. An important part of the development of these tools is physically based, fast and flexible compact models for devices such as integrated lasers. In this paper we will present the integration of a travelling wave based laser model into a time-domain SPICE compatible optoelectronic circuit/system simulator (OptiSPICE) and its application to a mixed domain circuit containing an optical ring switch and driver and detector circuits.
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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) | 0.000 | 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) | 0.010 | 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".