Bibliographic record
Abstract
ABSTRACT It should be noted that this is an overview paper that represents the early stages of an ongoing investigation into the causes and effects between conductive anodic filament (CAF) formation and printed wiring board (PWB) material damage. Our belief is that certain or specific types of material damage can increase the propensity for CAF formation. The preliminary data collected suggests is that there is no statistical correlation between the general definition of material damage (cohesive failure) and CAF. The resulting dichotomy is that we find no CAF failures in some coupons that have obvious material damage and we find CAF failures in coupons that don’t exhibit material damage. Since the advent of the European Union’s legislation for Restriction on Hazardous Substances (RoHS) lead (Pb) was removed from solder in surface finishes and pastes used in the component assembly process. The alternative metals and alloys to traditional tin/lead (Sn/Pb) solder required that the assembly temperatures be increased to achieve the higher melting point of the lead free solders. The traditional assembly temperature reached a level of 230°C, lead-free can require up to a maximum of 260°C, although most assembly houses are using a more modest 245°C. Multiple exposures to the additional 15°C to 30°C has demonstrated a negatively impact to the integrity of the FR4 and halogen free dielectric material used in PWB'substrates. Quantification of material damage is now possible through new techniques that utilize capacitance measurements to identify specific levels of bulk capacitance change that signify degradation within the resin system. This technique was employed to non-destructively identify both the locations within the construction and the magnitude of the change, traditional microsectioning was completed to confirm the results of the capacitance testing. This new technique, including equipment used is described Many of the commercially available materials have not demonstrated sufficient robustness when exposed to multiple lead-free assembly and rework thermal excursions. The reality is that these higher assembly and rework temperatures are increasing the risk of material damage. One would naturally expect that the increasing levels of material damage would produce an opportunistic path that would provide an increased possibility for CAF growth. In order to understand this very complex environment it is necessary to lay the ground work for how and effective quantification can be determined. This paper reviews the results of some initial work, our strategy for improved test vehicles design, including features for measuring material damage and CAF formation, the assembly and rework environments, the material and CAF testing methodology and the protocols that will be used. Our ultimate objective is to establish whether correlation can be found between the various types of material damage and the propensity to CAF failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".