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Record W4281560993 · doi:10.1364/oe.460533

Integrated 1 × 3 MEMS silicon nitride photonics switch

2022· article· en· W4281560993 on OpenAlexafffund
Suraj Sharma, Niharika Kohli, Jonathan Brière, Frédéric Nabki, Michaël Ménard

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsActive Ethernet Passive Optical Networks Over λ MultipleXing (Canada)CMC Microsystems (Canada)École de Technologie Supérieure
FundersPRIMA QuébecNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofabricationMicroelectromechanical systemsInsertion lossMaterials scienceSilicon nitrideOptical switchPhotonicsOptoelectronicsSilicon on insulatorOpticsSilicon photonicsSiliconFabricationPhysics

Abstract

fetched live from OpenAlex

We present a 1 × 3 optical switch based on a translational microelectromechanical system (MEMS) platform with integrated silicon nitride (SiN) photonic waveguides. The fabricated devices demonstrate efficient optical signal transmission between fixed and suspended movable waveguides. We report a minimum average insertion loss of 4.64 dB and a maximum average insertion loss of 5.83 dB in different switching positions over a wavelength range of 1530 nm to 1580 nm. The unique gap closing mechanism reduces the average insertion loss across two air gaps by a maximum of 7.89 dB. The optical switch was fabricated using a custom microfabrication process developed by AEPONYX Inc. This microfabrication process integrates SiN waveguides with silicon-on-insulator based MEMS devices with minimal stress related deformation of the MEMS platform.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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