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Record W2158969233 · doi:10.1109/stherm.1992.172851

Analysis of thermal vias in high density interconnect technology

2003· article· en· W2158969233 on OpenAlexaff
S. Lee, T.F. Lemczyk, M. M. Yovanovich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInterconnectionMaterials scienceThermalOptoelectronicsComputer scienceElectronic engineeringElectrical engineeringTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

An analytical approach is presented for the thermal modeling of via networks used in removing heat from chips in high density multichip module designs. The thermal resistances of the components making up a typical via network cell are accurately determined by closed form expressions. The complete thermal resistance between the die and substrate can be determined by constructing the unit cells in a combination of series and parallel paths, allowing for the thermal spreading effect through the via network, and the epoxy and planarizing layer thermal resistances. Computed predictions are compared with numerical and experimental results, and good agreement was achieved by using this accurate and simple 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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations54
Published2003
Admission routes1
Has abstractyes

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