Process Optimization for 1.0 mm Pitch CBGA
Bibliographic record
Abstract
ABSTRACT The ceramic ball grid array (CBGA) package is used today in a wide range of applications because of its many advantages: high interconnection density; compatibility with standard surface mount technology (SMT) assembly techniques; excellent thermal and electrical performance; and high interconnectivity. Todayís CBGA applications include memory, logic, and microprocessors, and they are found in computer systems ranging from desktop to mainframe with highest usage in work station and server applications. As these applications, particularly the high-end work stations and network servers, demand greater numbers of interconnections, increasing the interconnection density is one means of providing a solution. Decreasing the interconnection pitch on the CBGA package from the typical 1.27 mm to 1.0 mm gives a corresponding increase in interconnection density. This increased density reduces the package body size for the same number of interconnections. For instance, 937 I/O are available on a 32.5 mm package with 1.0 mm pitch, instead of a 42.5 mm package with 1.27 mm pitch (1088 I/O). The smaller body size conserves area on the printed circuit board, but the tighter pitch requires more advanced printed circuit board (PCB) groundrules. The smaller body size, with its reduced distance to neutral point (DNP), should improve the reliability on a per interconnection basis, although the advantage is tempered by a reduction in solder joint height due to a smaller solder ball. The appropriate geometries and process parameters must be chosen, however, for optimum manufacturing yield and reliability. This paper will discuss the process development activities and experiments to determine the optimum structure for the 1.0 mm pitch CBGA package. Elements included in the evaluation are: printed circuit board groundrules, ball diameter and attach processing, solder volumes and other card assembly parameters, and card assembly rework processing. Both yield and reliability are considered in choosing the optimum structure and processes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".