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Record W2007691457 · doi:10.1109/lpt.2014.2377176

Monolithic <inline-formula> <tex-math notation="LaTeX">$1\times 2$ </tex-math></inline-formula> MMI-Based 25-Gb/s SOI DPSK Demodulator Integrated With SiGe Photodetector

2014· article· en· W2007691457 on OpenAlexafffund
Mohammed Shafiqul Hai, Meer Sakib, Odile Liboiron-Ladouceur

Bibliographic record

VenueIEEE Photonics Technology Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsDemodulationSilicon on insulatorPhysicsBit error ratePhotodetectorPhase-shift keyingExtinction ratioOptoelectronicsMaterials scienceOpticsElectronic engineeringElectrical engineeringComputer scienceSiliconDecoding methodsAlgorithmEngineering

Abstract

fetched live from OpenAlex

In this letter, the performance of a 25-Gb/s 1 × 2 multimode interference (MMI) coupler-based monolithic differential phase shift keying (DPSK) receiver integrated with Germanium (Ge) on silicon-on-insulator (SOI) photodetector (PD) is presented. Theoretical analysis suggests that the device exhibits more uniform extinction ratio over the C-band compared with a DPSK demodulator based on 2×2 MMI coupler. Electrical scattering parameters (S-parameters) investigated through component level simulation comparing the fabrication tolerance shows power penalty of 1.2 and 5 dB for unbalanced and balanced detection DPSK receiver, respectively. The SOI 1×2 MMI coupler-based unbalanced DPSK receiver has a sensitivity of 0.41 dBm at a bit error rate of 1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> without postelectrical amplification. The PD integrated single-end detectionbased receiver has a total footprint area of 0.39 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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
Published2014
Admission routes2
Has abstractyes

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