Assessing the Manufacturability & Reliability of Fine Pitch Array Packages: An in-Depth Study
Bibliographic record
Abstract
ABSTRACT The application of fine pitch packaging in the electronics industry has grown significantly in the past several years. A number of studies have focused on optimization of design and assembly process variables, however the contribution of these factors to solder joint reliability is not as clearly understood. A study has been undertaken to evaluate the robustness of assembly processes and the solder joint reliability of a test vehicle platform containing thirty-one unique fine-pitch packages. One goal was to evaluate a wide variety of commercially available fine pitch packaging technologies on a test vehicle platform that is representative of a mixed technology production assembly. A second goal was to evaluate the impact of a variety of experimental factors on the thermo-mechanical reliability of the packages under test. Specific experimental factors included the effect of board thickness; the effect of surface finish; the effect of pad size; and the effect of component overlap (mirror image). Taguchi methods were used to evaluate the significance of each of these factors on the reliability of these packages. Specific component reliability comparisons were made comparing the reliability of land grid array (LGA) vs. ball grid array (BGA) packages. Failure analysis was completed to identify the failure modes of these components in the study.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".