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Record W4285287110 · doi:10.1109/lpt.2022.3185925

CMOS-Compatible and Temperature Insensitive C-Band Wavelength (De-)multiplexer

2022· article· en· W4285287110 on OpenAlexaff
Mao Deng, Yun Wang, Luhua Xu, Jinsong Zhang, Eslam El‐Fiky, Md Samiul Alam, Yannick D’Mello, Stéphane Lessard, David V. Plant

Bibliographic record

VenueIEEE Photonics Technology Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsEricsson (Canada)CMC Microsystems (Canada)McGill University
Fundersnot available
KeywordsMultiplexerOptoelectronicsMaterials scienceCMOSSilicon on insulatorWavelength-division multiplexingPhotonicsDemultiplexerMultiplexingSilicon photonicsOpticsWavelengthPhysicsSiliconComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

The high thermal sensitivity of the Silicon Photonics (SiP) platform compromises the performance of variant devices and increases the power consumption to stabilize the temperature. To address this issue, we demonstrate a CMOS-compatible and temperature insensitive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> C-band wavelength division (de-)multiplexer on the 220-nm-thick silicon-on-insulator platform. The (de-)multiplexer design is based on cascading Mach-Zehnder interferometers (MZIs). The waveguide widths of the MZI delay lines are matched to decrease the overall thermo-optic coefficient (TOC). For comparison, an MZI-based (de-)multiplexer with uniform delay lines is also fabricated on the same chip. The transmission spectra of the proposed and reference devices are measured when the wavelength is swept from 1500 nm to 1600 nm and the temperature is varied from 293.15 K to 323.15 K. The measured results show that the TOCs of the proposed and reference device are 4.8 pm/K and 85 pm/K, respectively. This power-efficient multiplexer with high integration density is promising for data center applications.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.239
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.189
Teacher spread0.184 · 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 teacher head, 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

Citations3
Published2022
Admission routes1
Has abstractyes

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