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Record W2887525176 · doi:10.1109/ectc.2018.00247

Smart Packaging - Microscopic Temperature and Moisture Sensors Embedded in a Flip-Chip Package

2018· preprint· en· W2887525176 on OpenAlexafffund
Aurore Quelennec, Yosri Ayadi, Quentin Vandier, Éric Duchesne, H. Frémont, Dominique Drouin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsIBM (Canada)Institut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlip chipDelamination (geology)Materials scienceMoistureReliability (semiconductor)InterconnectionSystem in packageIntegrated circuit packagingCarbon nanotubeChipPrinted circuit boardElectronic engineeringComposite materialIntegrated circuitOptoelectronicsComputer scienceElectrical engineeringTelecommunicationsLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

The interest in very-large-scale integration technology combined with market pressure to reduce materials and process costs introduce new packaging yield and reliability challenges. The use of organic substrates in flip-chip packages, rather than ceramics, led to thermal and moisture related issues. The organic laminate coefficient of thermal expansion substantially differs from the chip one, potentially leading to interfacial delamination and interconnect rupture. Moreover, organic substrates are permeable to water, potentially leading to electrochemical migration, corrosion of alloys, delamination and short-circuits. Thus temperature and moisture variation can have baneful consequences on flip-chip packages. Here, the internal module temperature and moisture quantities of a flip-chip package are measured using microscopic embedded carbon nanotube-based sensors. Those sensors are fabricated near interconnects and are positioned to provide spatial and real-time mapping of moisture and temperature in the package, for a better interconnect reliability study or package aging monitoring.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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.

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

Citations5
Published2018
Admission routes2
Has abstractyes

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