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Record W2775687163 · doi:10.1109/jlt.2017.2779757

RF and Thermal Considerations of a Flip-Chip Integrated 40+ Gb/s Silicon Photonic Electro-Optic Transmitter

2017· article· en· W2775687163 on OpenAlexaff
Zheng Yong, Stefan Shopov, Jared C. Mikkelsen, R. E. Mallard, Jason C. C. Mak, Junho Jeong, Sorin P. Voinigescu, Joyce K. S. Poon

Bibliographic record

VenueJournal of Lightwave Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCMC Microsystems (Canada)Queen's UniversityUniversity of Toronto
Fundersnot available
KeywordsTransmitterSilicon photonicsFlip chipMaterials scienceElectro-optic modulatorPhotonicsOptoelectronicsCMOSOptical interconnectSiliconOpticsOptical modulatorElectrical engineeringPhase modulationEngineeringPhysicsTelecommunicationsInterconnectionChannel (broadcasting)

Abstract

fetched live from OpenAlex

We demonstrate a flip-chip integrated electro-optic transmitter incorporating a high-swing CMOS driver and silicon photonic Mach-Zehnder modulator, and discuss the RF and thermal characteristics of the assembly. The transmitter showed a dynamic extinction ratio of 8 dB, which is the highest to date for 40+ Gb/s-class transmitters using CMOS drivers with silicon modulators. The input reflection coefficient of the module was mostly determined by the input electrical lines, solder bumps, and driver, while the electro-optic transfer function was mostly set by the Mach-Zehnder modulator. Despite the high power consumption of the driver and modulator (553 mW) and the close proximity between the electronic and photonic dies, the thermal simulations show that heat can be efficiently sunk from the bottom side of the silicon photonic die.

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 score0.012
Threshold uncertainty score0.465

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 head0.009
GPT teacher head0.227
Teacher spread0.217 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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