Analytical k-Factor Model for Monotonic Four-Point Bend Test Design
Bibliographic record
Abstract
Advanced electronic systems are increasingly integrated into various transportation, communication, and industry applications. The mechanical reliability of these electronic systems, especially their solder interconnects, is vital. The mechanical strength of the package and board interaction with the solder joint is often characterized by monotonic four-point bend testing. The bending strength of the interconnects relates to their ability to handle mechanical loading from assembly, test, handling and field-use operations. Recently, there are efforts to better understand the influence of printed circuit board assembly (PCBA) and monotonic four-point bend test parameters on solder joint interconnect mechanical strength. In addition, a better correlation between the global strain and local critical strain is desired. The purpose of this work is to develop an analytical model capable of quickly and accurately predicting response in monotonic four-point bend test design for ball grid array (BGA) devices. Models were developed for strain intensity factors k1and k2 based on five key parameters identified by Spearman rank correlation analysis. The models were observed to be highly accurate with 5% and 4% error for k1and k2, respectively, when compared to randomly excluded test points. The model will be referenced in the next IPC/JEDEC-9702 revision, improving accuracy and repeatability of the test method. Additionally, the model can be used by designers, early in the test board design process to assess flexural bending strength impacts of design changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".