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Record W4416885061 · doi:10.37665/srrwfps64329

Evaluation of Thermal Stress in a Flip Chip Package by Hybrid Experimental-Analytical Method

2009· article· W4416885061 on OpenAlexaff
Alireza Sahami Shirazi, Hua Lu, A. Varvani‐Farahani

Bibliographic record

VenueSoldering and Reliability Conferences · 2009
Typearticle
Language
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFlip chipBall grid arrayInterconnectionResidual stressIntegrated circuit packagingThermalChipShearing (physics)Electronic packaging

Abstract

fetched live from OpenAlex

ABSTRACT This article presents an application of a Hybrid Experimental-Analytical Inverse Method (HEAIM) to the evaluation of thermo-mechanical behavior of a flip-chip package. The method is recently developed and is generally applicable to tri-material adhesively bonded beam type plates in design for package reliability. The assembly induced interconnect residual stresses (peeling and shear stresses) are major concerns since they are directly responsible for the manufacturing induced interconnect defects and failure in such packages. In this particular application, measurements of thermal warpage at different temperatures of a Flip Chip (FC) Plastic Ball Grid Array (PBGA) package are obtained through a phase-shifted shadow moir’ experiment. The warpage measurement results between layers were used as inputs to the 2D- plane strain linear-elastic solution proposed earlier by Suhir. Using the inverse method, the effective thermo-mechanical behavior of layers in a tri-layer laminate was analytically assessed. The analytical solution for package warpage is validated by experimentally obtained value of thermal warpage. The solution offers a quantitative evaluation of the shearing and peeling stresses as the tri-material package cools down from the elevated temperature to the room temperature.

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)0.004
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.314
Teacher spread0.283 · 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 designSimulation or modeling
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

Citations0
Published2009
Admission routes1
Has abstractyes

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