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Record W2110729165 · doi:10.3390/photonics2041065

A Thermally Tunable 1 × 4 Channel Wavelength Demultiplexer Designed on a Low-Loss Si3N4 Waveguide Platform

2015· article· en· W2110729165 on OpenAlexafffund
Mohammed Shafiqul Hai, Arne Leinse, T.T. Veenstra, Odile Liboiron-Ladouceur

Bibliographic record

VenuePhotonics · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersCanada Research ChairsCMC Microsystems
KeywordsDemultiplexerInsertion lossMaterials scienceWaveguideWavelength-division multiplexingChannel spacingOpticsOptoelectronicsInterferometryFree spectral rangePhotonic integrated circuitOptical switchWavelengthPhotonicsMultiplexingPhysicsTelecommunicationsMultiplexerComputer science

Abstract

fetched live from OpenAlex

A thermally tunable 1 × 4 channel optical demultiplexer was designed using an ultra low-loss Si3N4 (propagation loss ~3.1 dB/m) waveguide. The demultiplexer has three 2 × 2 Mach-Zehnder interferometers (MZI), where each of the MZI contains two 2 × 2 general interference based multimode interference (MMI) couplers. The MMI couplers exhibit −3.3 dB to −3.7 dB power division ratios over a 50 nm wavelength range from 1530 nm to 1580 nm. The chrome-based (Cr) heaters placed on the delay arms of the MZI filters enable thermal tuning to control the optical phase shift in the MZI delay arms. This facilitates achieving moderately low crosstalk (14.5 dB) between the adjacent channels. The optical insertion loss of the demultiplexer per channel is between 1.5 dB to 2.2 dB over the 1550 nm to 1565 nm wavelength range. Error free performance (BER of 10−12) is obtained for all four 40 Gb/s data rate channels. The optical demultiplexer is an important tool towards building photonic integrated circuits with complex optical signal processing functionalities in the low-loss Si3N4 waveguide 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

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)0.0010.000

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 head0.030
GPT teacher head0.232
Teacher spread0.202 · 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
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
Published2015
Admission routes2
Has abstractyes

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