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Record W1638369295 · doi:10.1109/lpt.2015.2462341

Michelson Interferometer Thermo-Optic Switch on SOI With a 50-µW Power Consumption

2015· article· en· W1638369295 on OpenAlexafffund
Zeqin Lu, K. Murray, Hasitha Jayatilleka, Lukas Chrostowski

Bibliographic record

VenueIEEE Photonics Technology Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilCMC Microsystems
KeywordsSilicon on insulatorOptical switchExtinction ratioMaterials scienceOptoelectronicsInterferometryMichelson interferometerOptical isolatorOpticsPower consumptionSilicon photonicsSwitching timePhotonicsPower (physics)PhysicsSiliconOptical fiberWavelength

Abstract

fetched live from OpenAlex

We demonstrate photonic ultra-efficient thermo-optic switches on a 220-nm silicon-on-insulator platform. We used several approaches to increase the tuning efficiency of the switches. We used folded waveguides in a Michelson interferometer configuration to increase the optical interaction length of the light with the heated region, and used a suspended structure to improve thermal isolation. An ultra-low switching power of 50 μW is realized with an extinction ratio of over 26 dB for the transverse electric mode at 1550 nm. The 10%-90% response time of the switch is 1.28 ms, including a 780 μs rise time and a 500 μs fall time. Compared with the best thermo-optic switch in the literature, our device shows approximately an order of magnitude reduction in power consumption.

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 score0.007

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.221
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

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

Citations54
Published2015
Admission routes2
Has abstractyes

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