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Record W3007821222 · doi:10.1117/12.2549959

Packaging challenges for next-generation high bandwidth opto-electrical switch modules

2020· article· en· W3007821222 on OpenAlexaff
Alexander Janta-Polczynski, Elaine Cyr, Richard N. Langlois, Nicolas Boyer, Paul Fortier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsOptical switchSilicon photonicsPhotonicsBandwidth (computing)Optical interconnectOptical cross-connectComputer scienceOptical fiberOptical engineeringCable glandInterposerTransceiverBall grid arrayElectronic engineeringInterconnectionOptoelectronicsMaterials scienceTelecommunicationsEngineeringOpticsNanotechnologyPhysicsSoldering

Abstract

fetched live from OpenAlex

It is anticipated that once silicon switch I/Cs reach 51.2Tbps, there will be a need to migrate from electrical I/Os to optical I/Os. This drives the need to co-package optical engine transceivers with the high-performance silicon switch. In this paper, we explore the challenges, compare the various solutions, and provide guidance from an assembly and optical connector design perspective. Our focus is on single-mode optic solutions privileged for mid to long range optical links. Topics covered include optical interconnect density to the silicon photonics chip, integrated optical interconnects vs pigtail fiber ribbons, socketable vs. μBGA optical engines, laser source location and special requirements for the optical connectors. The ability to embed optics in the first level package is a disruptive capability needed to meet the everincreasing bandwidth demands for data communication. The benefits of such configurations are discussed, as well as the challenges for thermal management and system yields.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.445

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 integrity0.0000.000
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 head0.089
GPT teacher head0.234
Teacher spread0.146 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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