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Record W2742359771 · doi:10.1109/ectc.2017.140

Smart Packaging: A Micro-Sensor Array Integrated to a Flip-Chip Package to Investigate the Effect of Humidity in Microelectronics Package

2017· preprint· en· W2742359771 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsIBM (Canada)Institut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMicroelectronicsMaterials scienceFlip chipWaferReliability (semiconductor)Integrated circuit packagingQuad Flat No-leads packageSystem in packageHumidityElectronicsElectronic packagingChip-scale packageOptoelectronicsChipElectronic engineeringIntegrated circuitElectrical engineeringNanotechnologyComposite materialEngineeringLayer (electronics)

Abstract

fetched live from OpenAlex

Lifetime reliability of electronics package is important for long term operation of microelectronic devices. Humidity, temperature and resulting strain are three main reasons for the failure of a flip-chip semiconductor package. For these reasons, continuous research effort have been devoted to integrate moisture, temperature and strain sensors in package performance and failure risk monitoring. We here demonstrate combined humidity, strain and temperature sensors based on a carbon nanotube mesh embedded in a polyimide matrix. We further demonstrate that an array ofmicro-humidity(/strain/temperature) sensors can be integrated at the wafer finish step of semiconductor chips to provide useful in-situ real-time information.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.004
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 head0.012
GPT teacher head0.250
Teacher spread0.239 · 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

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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