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Record W4285589045 · doi:10.1515/nanoph-2022-0141

Polymer modulators in silicon photonics: review and projections

2022· review· en· W4285589045 on OpenAlexafffund
Iman Taghavi, Maryam Moridsadat, Alexander Tofini, Shaheer Raza, Nicolas A. F. Jaeger, Lukas Chrostowski, Bhavin J. Shastri, Sudip Shekhar

Bibliographic record

VenueNanophotonics · 2022
Typereview
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsQueen's UniversityUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSilicon photonicsMaterials scienceOptical modulatorPhotonicsComputer scienceVoltageOptoelectronicsCMOSElectronic engineeringPhase modulationElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Optical modulators are vital for many applications, including telecommunication, data communication, optical computing, and microwave photonic links. A compact modulator with low voltage drive requirement, low power, high speed, and compatibility with CMOS foundry process is highly desirable. Current modulator technologies in Si suffer from trade‐offs that constrain their power, performance (speed, drive voltage), and area. The introduction of additional materials to the silicon platform for efficient phase shift promises alternatives to relax those trade‐offs. Si‐organic‐hybrid (SOH) devices demonstrate large modulation bandwidth leveraging the electro‐optic (EO) effect and smaller drive voltage or footprint owing to a strong EO coefficient. In this study, we review various SOH modulators and describe their path towards integration to silicon, including their challenges associated with aging and temperature. We also briefly discuss other high‐performance modulators such as plasmonic‐organic‐hybrid (POH), photonic‐crystal‐assisted SOH, and LiNbO 3 .

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2022
Admission routes2
Has abstractyes

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