Simulation approach to improving BGA reliability on coreless packages
Bibliographic record
Abstract
With the increased popularity of ultra-portable electronics such as laptops, microprocessor manufacturers have had to move away from the conventional and highly reliable pin-grid array (PGA) packages and towards ball-grid array (BGA) packages for this market segment due to thickness restrictions. This shift brings with it some reliability concerns. In addition, to shrink the form factor and improve electrical performance further, standard-core substrates are being swapped for thin-core and coreless variations. This work evaluates BGA performance of flip-chip packages with coreless substrates through finite element analysis (FEA) simulation. A three-dimensional quarter-model of a package with no heat spreader on coreless substrate with mixed BGA pitch was used so the location of expected failure can be simulated more accurately. This work then proposes a methodology for improving BGA reliability of coreless packages. Taking into account the behavior of coreless BGA packages, it is proposed that one possible method to improve BGA life in these packages would be to convert the few critical joints to dummy (power/ground plane) joints such that failure of the critical die corner joints does not result in failure of the part. This can be implemented by a design rule that stipulates the replacement of critical die corner joints with dummy joints in coreless substrates. We determined expected percentage improvement in BGA life with the implementation of such a design rule using FEA simulations and Miner's rule: for the BGA layout assumed here, results indicate that 130% improvement in BGA life is possible when five solder joints at the die corner are replaced with dummy joints. This work will be useful for robust design of solder joints in BGA packages with coreless substrates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".