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Record W2789555118 · doi:10.1002/lpor.201700237

Silicon Photonics Circuit Design: Methods, Tools and Challenges

2018· article· en· W2789555118 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLaser & Photonics Review · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersVlaamse regeringFonds Wetenschappelijk Onderzoek
KeywordsSchematicPhotonicsDesign flowElectronic design automationCircuit designComputer scienceDesign toolPhotonic integrated circuitElectronic circuitElectronic engineeringComputer architectureAbstractionIntegrated circuit designElectronic componentElectronicsEngineeringElectrical engineeringEmbedded systemPhysicsMechanical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Silicon Photonics technology is rapidly maturing as a platform for larger‐scale photonic circuits. As a result, the associated design methodologies are also evolving from component‐oriented design to a more circuit‐oriented design flow, that makes abstraction from the very detailed geometry and enables design on a larger scale. In this paper, the state of this emerging photonic circuit design flow and its synergies with electronic design automation (EDA) are reviewed. The design flow from schematic capture, circuit simulation, layout and verification is covered. The similarities and the differences between photonic and electronic design, and the challenges and opportunities that present themselves in the new photonic design landscape, such as variability analysis, photonic‐electronic co‐simulation and compact model definition are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.318
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it