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Record W2551717087 · doi:10.1109/tcsi.2016.2613514

CMOS-Photonics Codesign of an Integrated DAC-Less PAM-4 Silicon Photonic Transmitter

2016· article· en· W2551717087 on OpenAlexafffund
Hassan Sepehrian, Amin Yekani, Leslie A. Rusch, Wei Shi

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2016
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSTransmitterPhotonicsSilicon photonicsElectronic engineeringModulation (music)Computer scienceElectrical engineeringChannel (broadcasting)OptoelectronicsEngineeringMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Codesign and integration of optical modulators and CMOS drivers is crucial for high-speed silicon photonic (SiP) transmitters to reach their full potential for low-cost, low-power electronic-photonic integrated systems. We present a CMOS-driven SiP multi-level optical transmitter implemented using a commercially available lateral p-n junction process. It uses a Mach-Zehnder modulator (MZM) segmented to increase speed and to lower the required power on a per segment basis to a level achievable with CMOS. A multi-channel driver is designed and implemented in 130 nm RF CMOS, providing a swing of 4 V in a push-pull configuration at 20 Gbaud. Binary data at the CMOS input is manipulated via digital logic to produce the proper per-segment drive signals to generate a four-level pulse-amplitude modulation optical signal. Multi-level modulation is achieved using only binary signals as input (DAC-less). Cosimulation of the optical and electrical circuits shows good agreement with experiment. Reliable transmission is achieved without post-compensation at 28 Gb/s, and at 38 Gb/s when using post-compensation.

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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.015
GPT teacher head0.201
Teacher spread0.186 · 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

Citations33
Published2016
Admission routes2
Has abstractyes

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