DOE for Process Validation Involving Numerous Assembly Materials and Test Methods
Bibliographic record
Abstract
ABSTRACT Selecting products that have been qualified by industry standards for use in printed circuit board assembly processes is an accepted best practice. That products which have been qualified, when used in combinations not specifically qualified, may have resultant properties detrimental to assembly function though, is often not adequately understood. Printed circuit boards, solder masks, soldering materials (flux, paste, cored wire, rework flux, paste flux, etc.), adhesives, and inks, when qualified per industry standards, are qualified using very specific test methods which may not adequately mimic the assembly process ultimately used. It is recommended that products used in combination on a printed circuit assembly be qualified in combination to the extent necessary to provide a full understanding of interactions that may occur in the product. IPC J-STD-001 provides good guidance with regards to process validation testing although said testing is limited to the Appendix of the document. J-STD-001D, Appendix C focuses on process validation via Surface Insulation Resistance (SIR) Testing. The limitation to relying solely on SIR testing is its inherent inability to detect possible assembly issues which could result from employed combinations of printed circuit boards, solder masks, soldering materials (flux, paste, cored wire, rework flux, paste flux, etc.), adhesives, and inks. The authors’ intention with this paper is to describe two Designs of Experiments (DOE’s) that were developed for process validation. The first relies on SIR testing as the sole means of qualifying the final assembly. The second explores the use of a broad range of tests chosen to closely represent the end use application. DOE #1 was based upon the SIR testing procedure as per ANSI/J-STD-004, IPC-TM-650 2.6.3.3A the method which is also specified in J-STD-001. The scope of the test method is to determine the degradation of electrical insulation resistance of PCB specimens after exposure to specified materials. Since during the normal manufacturing process, a board assembly is exposed to a number of chemicals, for this DOE the standard SIR test coupons were prepared in such a way as to mimic a “typical” chemical mix that a board assembly may come in contact with during assembly. This involved applying the appropriate chemicals in the typical process order using appropriate process conditions.
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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 | 0.029 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".