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Record W2040399487 · doi:10.1561/1000000014

System-in-Package: Electrical and Layout Perspectives

2011· article· en· W2040399487 on OpenAlexaff
Lei He, Shauki Elassaad, Yiyu Shi, Yu Hen Hu, Wei Yao

Bibliographic record

VenueFoundations and Trends® in Electronic Design Automation · 2011
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScalabilityComputer scienceSystem in packageElectronicsSystem integrationDesign flowEmbedded systemRouting (electronic design automation)Power integritySignal integrityPhysical designSystems designComputer architectureTelecommunicationsComputer hardwareElectrical engineeringSystems engineeringPrinted circuit boardEngineeringCircuit designSoftware engineeringOperating systemChip

Abstract

fetched live from OpenAlex

The unquenched thirst for higher levels of electronic systems integration and higher performance goals has produced a plethora of design and business challenges that are threatening the success enjoyed so far as modeled by Moore’s law. To tackle these challenges and meet the design needs of consumer electronics products such as those of cell phones, audio/video players, digital cameras that are composed of a number of different technologies, vertical system integration has emerged as a required technology to reduce the system board space and height in addition to the overall time-to-market and design cost. System-in-package (SiP) is a system integration technology that achieves the aforementioned needs in a scalable and cost-effective way, where multiple dies, passive components, and discrete devices are assembled, often vertically, in a package. This paper surveys the electrical and layout perspectives of SiP. It first introduces package technologies, and then presents SiP design flow and design exploration. Finally, the paper discusses details of beyond-die signal and power integrity and physical implementation such as I/O (input/output cell) placement and routing for redistribution layer, escape, and substrate.

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 score0.793
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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