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Record W4402536754 · doi:10.1117/1.oe.63.9.097101

Silicon microring resonator modulator based on PIN junction: performance optimization

2024· article· en· W4402536754 on OpenAlexaff
Niloofar Mohamadi, Mohammad Razaghi, Omid Jafari

Bibliographic record

VenueOptical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité LavalUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsResonatorMaterials scienceOptoelectronicsSiliconSilicon photonicsOptical engineeringIntegrated opticsOpticsModulation (music)Optical modulatorPhase modulationPhase noisePhysics

Abstract

fetched live from OpenAlex

We modeled and investigated the silicon microring modulator (SMM) based on a PIN junction. The proposed model’s results are in good agreement with the experimental results. The effects of various parameters were considered in an optimized model to decrease power consumption as well as increase the bandwidth and extinction ratio of the SMM. The power consumption parameter plays a key role in the modulator design. To decrease this parameter, modulator dimensions were optimized using a genetic algorithm. This was done by considering the effect of the quality factor on the gradient change of the SMM transmission profile. In comparison with experimental results, the power consumption in the presented optimized model decreased dramatically from 9.7 to 0.042 μW. In addition, the extinction ratio and bandwidth increased by 12.5% and 50%, respectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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 designBench or experimental
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 routes1
Has abstractyes

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