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Board Level Life Assessment of Large Body Flip Chip Packages with Smaller Solder ball pitch & Methodologies to improve Board level Reliability

2022· article· en· W4317381708 on OpenAlexaff
Anandan Ramasamy, Inderjit Singh, Ace Ng, Shin Low, Gerry Maloney, Alan Shao

Bibliographic record

Venue2022 IEEE 24th Electronics Packaging Technology Conference (EPTC) · 2022
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsFlip chipPrinted circuit boardTemperature cyclingBall grid arraySolderingDie (integrated circuit)Chip-scale packageReliability (semiconductor)Small form factorReflow solderingQuad Flat No-leads packageAutomotive engineeringChipMechanical engineeringEngineeringElectronic engineeringMaterials scienceElectrical engineeringThermalLayer (electronics)Power (physics)Composite material

Abstract

fetched live from OpenAlex

Continuously expanding flip chip package market is driven by Data Centre applications, 5G wireless, network devices, healthcare, industrial, vision and automotive applications. The demand for high performance, low power, small form factor, more IOs in a small real estate, smaller bump & solder ball pitches, high layer count substrates is posing engineering challenges to contain package warpage and at the same time to improve working life of packages in end user applications. In this context, one critical part for the overall reliability of packages is the performance of Package to Board solder joints under thermal mechanical stresses. For board level reliability assessment, design of experiment was done using test vehicles using Flip chip package with 7nm silicon device. Test packages were assembled using flip chip assembly process using a multilayer substrate. Assembled packages were mounted on Printed Circuit boards with lead free reflow profile. Board level thermal cycling was done using a thermal cycling chamber. This paper discusses the Design of Experiments, experiment set up, layout of packages on the board, characteristics life using Weibull model, board level life assessment and methodologies to enhance board level solder joint performance

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.229
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.052
GPT teacher head0.306
Teacher spread0.254 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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