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Record W1655909695

A study on high-density high-speed SerDes design in buildup flip chip ball grid array packages

2009· article· en· W1655909695 on OpenAlexaff
Gordon Xiang, Keith Sheach, Pierre Brunet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsSTMicroelectronics (Canada)
Fundersnot available
KeywordsBall grid arraySerDesFlip chipQuad Flat No-leads packageElectronic engineeringHFSSComputer scienceElectrical engineeringMaterials scienceEngineeringLayer (electronics)SolderingMicrostrip antenna
DOInot available

Abstract

fetched live from OpenAlex

A study on high-density high-speed SerDes (HHS) designs in buildup laminate flip chip ball grid array (fcBGA) packages is presented in this paper. Experiences have shown that three main capacitive discontinuities happen in flip chip bump area, core PTH via area and BGA transition area. Literature [1] have studied three PTH via configurations for differential pairs and suggested that a routing structure with PTH via on the top of BGA be the best design option, which implies that all Serdes routings have to be done in buildup layers above the core layer. However, with growing Serdes number, limited buildup layer number and specified X-talk number, one cannot route all Serdes differential pairs in the buildup layers above the core layer. In a result, routing some Serdes differential pairs in buildup layers below the core becomes a must. In our paper, the three PTH via structures in [1] are analyzed in detail. In order to obtain a more realistic electrical performance, we include die bump, package wiring and BGA transition with a small portion of PCB transmission line in our models. Ansoft full-wave HFSS tool is used to run for S-parameters. Measurement data from a test system were used to guarantee the correctness of our model setup including boundary condition, port and material definitions and so on. Our studies have shown that all three PTH via configurations can be optimized and provide a similar level electrical performances up to 15GHz, which means a smaller buildup layer number and a package cost reduction for a given application. Numerical results for a 4-4-4 buildup package optimization are presented to show the merit of our methodology

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.994

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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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