MétaCan
Menu
Back to cohort
Record W3116018953 · doi:10.1149/ma2020-02251795mtgabs

Directly Visualized Mechanical Strain Distribution in Package Interconnect Using Digital Image Correlation

2020· article· en· W3116018953 on OpenAlexaff
Tae‐Ik Lee, Kyung‐Wook Paik, Taek‐Soo Kim

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsDigital image correlationDeformation (meteorology)InterconnectionDigital imageMaterials scienceSpeckle patternBendingComputer scienceImage processingComposite materialImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

For advanced package interconnects, analyzing strain distribution within the complex three-dimensional structure is important in order to design a mechanically reliable system. Electronic packages frequently undergo various types of deformation including tension, compression, bending, and thermal treatments. Failures under these loading modes can be prevented with accurate information of distribution and degree of the mechanical strains. This information, however, is difficult to obtain using conventional methods due to the small-sized packages with complex internal structures. It is also uncertain to rely on predictive results from analytical solutions because it is hard to theoretically calculate the complex strain mechanics, especially for the heterogeneously integrated structures. Therefore, experimental methods have been recently demanded for direct evaluation of mechanical deformation in the package interconnects. In this study, a novel method that visualizes and accurately measures the internal strain distribution in package interconnects is developed. This method is based on digital image correlation (DIC), which utilizes microscopic images of the internal structure. The DIC method is adopted by taking advantage of non-contact and full-field analysis capability. Images from both optical microscopy and scanning electron microscopy are utilized for the non-contact strain evaluation method for the micro-scale deformation analysis. In order to obtain clear image of the internal structure, mechanical polish is conducted to form cross-section. For image pattern tracking, micro/ nano sized particles are deposited onto the cross-section to serve as speckle pattern of the DIC analysis. After the specimen preparation, the digital images are obtained before and after a mechanical loading at a same position. The consecutive images are then compared to execute image tracking analysis to result in the microscopic deformation contour. The results can be fully adopted in prediction of failure site or failure modes by providing essential data such as weak spot or intensity of deformation. Using the quantitative strain information, predicting the failure cycle could be also possible based on mechanics of fatigue fracture. We demonstrate that this method is effectively utilized for various package interconnects including printed circuit boards (PCBs), flexible chip packages, and through-hole stress analysis in a 3-D stacked structure. Bending strain analysis of flexible packages and composite substrates, thermal strain analysis of the PCB interconnects are shown as applications of the thermo-mechanical reliability evaluation method. We believe that this experimental method will enable to fully understand the internal stress-strain behavior of the advanced package interconnects and flexible devices that require high mechanical reliability for commercialization.

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)0.001
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 score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.280
Teacher spread0.263 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvancements in Photolithography TechniquesFrench-language works237,207