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Record W2463658656 · doi:10.1109/jstqe.2016.2582345

Silicon-Photonics Microring Links for Datacenters—Challenges and Opportunities

2016· article· en· W2463658656 on OpenAlexafffund
Abdelrahman H. Ahmed, Ahmad Sharkia, Bryan Casper, Shahriar Mirabbasi, Sudip Shekhar

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilicon photonicsPhotonicsComputer scienceWavelength-division multiplexingEfficient energy useCMOSLink budgetMulti-mode optical fiberElectronic engineeringThroughputMultiplexingTransmitterOptoelectronicsChannel (broadcasting)Optical fiberMaterials scienceTelecommunicationsWavelengthElectrical engineeringEngineeringWireless

Abstract

fetched live from OpenAlex

The rapid growth of warehouse-scale datacenters demands high-throughput optical interconnects that can span short-to-medium reach distances (<; few kilometers). Microring-based silicon-photonics links with single-mode fibers are highly promising for these applications. This paper presents an analysis of microring-based links from the holistic perspective of optical devices, CMOS circuits, and system-level link budget and energy-efficiency simulations. Design considerations and tradeoffs for the receiver, transmitter, and the overall link are presented, and comparisons are made to the mainstream multimode vertical-cavity surface-emitting laser-based links with multi-mode fibers. Finally, research opportunities are highlighted for further improving the energy efficiency of single channel and wavelength division multiplexing-based silicon-photonics links.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.254
Teacher spread0.214 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations44
Published2016
Admission routes2
Has abstractyes

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