Water-Soluble Lead-Free Process Chemistry for High Voltage and High Reliability Hardware Requirements
Bibliographic record
Abstract
ABSTRACT Over the past decade, no-clean flux chemistry usage has continued to rise and is correlated with the transition to lead-free printed circuit board assembly adoption by consumer electronics market segments. It is estimated that well over 90% of lead-free hardware built today uses no-clean flux chemistry. Even before EU RoHS directive promulgation in July 2006, firms building consumer products were largely using no-clean based flux chemistries. With the required migration to lead-free assembly material usage, early adopter demand for no-clean lead-free chemistry solutions rose even further. During this same time, numerous RoHS exemptions have been in place; most notably exemption 7b “Lead in solder for server applications”. Firms manufacturing electronics using this exemption continued building product with SnPb solder. Despite an overall industry trend toward no-clean fluxes, many exempt SnPb built products employed water-soluble based assembly materials. Exercising allowable exemptions, firms building high complexity, high reliability products continued using SnPb water-soluble assembly materials and processes. Six years have now passed since original RoHS directive enforcement; products in several market sectors continue to exercise exemption 7b. As these product roadmaps now begin conversion activity, there is a growing need for lead-free water-soluble chemistry solutions. However, the major R & D investment by material suppliers has been made to offer no-clean lead-free chemistry supporting consumer segments. As a result, there are limited published data or options available to support lead-free water-soluble solutions for high complexity, high reliability hardware assembly. With this as background, IBM set out to examine the industry’s latest lead-free water-soluble chemistry options with the intent of selecting two top performing material sets for use with server and storage class hardware. This paper discusses details relating to the various tests conducted including manufacturability screening experiments, surface insulation resistance testing of IPC B-52 test coupons per IPC standards, and high voltage 550V / 2,150V hipot testing protocols. The result of this work yielded an end-to-end lead-free water-soluble chemistry solution, suitable for IBM’s highest complexity, highest reliability server class hardware systems.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".