MétaCan
Menu
Back to cohort
Record W2103568915 · doi:10.1109/tcpmt.2010.2100712

Transient Frequency-Domain Thermal Measurements With Applications to Electronic Packaging

2012· article· en· W2103568915 on OpenAlexaff
Yizhang Yang, R.N. Master, Gamal Refai-Ahmed, Maxat Touzelbaev

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsTransient (computer programming)Materials scienceFrequency domainElectronic packagingThermalComputer sciencePhysicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Non-uniform power distribution, increased die-size, and multiple-chip modules present new challenges for the thermal management of modern integrated circuit (IC) packages. Thermal characterization techniques capable of resolving partial thermal resistances at the component level have received increased emphasis in development of advanced packaging technologies. This paper aims to develop a practical method for thermal characterization of IC packages using the frequency-domain measurement technique as a complementary technique to the widely used time-domain thermal transient measurement technique. This paper discusses practical implementation of the technique and demonstrates both thermal modeling and experimental results. Thermal impedances measured in frequency-domain yield the structure function, which describes the dynamic thermal response of the device based on thermal RC network analysis. Various applications of this technique in thermal characterization of the IC packages subjected to field conditions are also discussed.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.220
Teacher spread0.204 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Components Packaging and Manufacturing TechnologySame topicElectronic Packaging and Soldering TechnologiesFrench-language works237,207