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Record W4400776470 · doi:10.1364/oe.525840

Enabling data-driven and bidirectional model development in Verilog-A for photonic devices

2024· article· en· W4400776470 on OpenAlexafffund
Dias Azhigulov, Zeqin Lu, James Pond, Lukas Chrostowski, Sudip Shekhar

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsLumerical Solutions (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaSchmidt Futures
KeywordsPhotonicsPhotonic integrated circuitComputer scienceReflection (computer programming)Electronic circuitOpticsElectronic engineeringPower (physics)VerilogPhotonic crystalPhysicsComputer hardwareEngineering

Abstract

fetched live from OpenAlex

We present a method to model photonic components in Verilog-A by introducing bidirectional signaling through a single port. To achieve this, the concept of power waves and scattering parameters from electromagnetism are employed. As a consequence, one can simultaneously transmit forward and backward propagating waves on a single wire while also capturing realistic, measurement-backed responses of photonic components in Verilog-A. We demonstrate examples to show the efficacy of the proposed technique in accounting for critical effects in photonic integrated circuits such as Fabry-Perot cavity resonance, reflections to lasers, reflection cancellation circuits, etc. Our solution makes electronic-photonic co-simulation more intuitive and accurate.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.038
GPT teacher head0.270
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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