Replacing Steam Preconditioning

1. Replacing Steam Preconditioning An ECA STC/IPC Designed Experiment Pathfinder Status Report Bill Russell Raytheon Professional Services LLC September 25, 2007

2. Introduction • Both the IPC Solderability Task Group and the ECA Soldering Technology Committee have agreed that a replacement is needed for the steam pre-conditioning step in the J-STD-002 solderability test • Purpose • To evaluate an alternative conditioning methodology that is more applicable to finishes we encounter today • Evaluate the effect of dry conditioning on component solderability performance • Assemble the data needed to make an informed decision • Candidate Preconditioning methods • Dry bake 4 hours at 155C (D04) • Dry bake 8 hours at 155C (D08) • Dry bake 16 hours at 155C (D16) • Condition at 72C, 85 %RH for 8 hours (W08) • Controls • Existing steam preconditioning (category 3, 8 hours) (SP) • As-received (AR)

3. The Full Designed Experiment Experimental Variables 16 pin Resistor network TSOP candidateDoug/Dave 7343 Molded-cap 0805 MLCC Part Type SOIC PDIP V-Chip Cap Lead frame Cu Cu Cu Brass Cu Steel/Cu Alloy 42 Lead Finish Sn NiPdAu SnPb NiPdAu Sn overNi Flash SnPb over Ni flash Sn SnPb Sn withNi barrier SnPb withNi barrier SnBi Sn SnPb Sn SnPb Response Variables Dip and Look – Estimate percent coverage (30 samples each) Wetting Balance – F@2 sec, Max force, Time to zero force, Time to 2/3 Max force(30 samples each) Assembly simulation – percent acceptable solder joints (30 samples each) Surface Species Analysis/Cross-section analysis (Dave/Doug to identify) – (3 samples each)

4. DOE Steps Prepare specimens Characterize Precondition specimens Solderability Test Analyze Results Assembly Simulation

5. Pathfinder study Steps: • Obtain specimens of the PDIP and resistor networks (1000 each min)– Doug/Dave April 1 • Retain extra samples for later analysis • Perform preconditioning - Dave • Divide into kits – Dave • Send out kits – Dave May 1 • Obtain balance of parts (1000 each min)– Dave/Doug Jun1 • Design or obtain a test board) – Dave • Perform solderability test with SAC305 (as-received w/o degradation step) and send results to Dave Aug 1 • Wetting balance - Gerard O’Brien, PCK • Dip and Look - Susan Hott, Robisan • Assembly (replicate w/Pb and Pb-free)– Dave, Rockwell Collins • Analyze results – Bill Sept 1 • Examine results at IPC Works Sept 24-28, 2007 • Plan remaining experiment – run experiment or create marginal part(s) Sept 24-28, 2007

6. Pathfinder Results • Test samples: 20 pin PDIP packages • Conditioning Methods • As Received • Steam preconditioning, 8 hours • Dry conditioning, 4, 8, 16 hours • Conditioning at 72C/85RH for 8 hours • Solderability test • SAC305 • Flux Actiec 2 • Measurement • Wetting balance • Time to zero force (seconds) • Force at 2 seconds mN/mm (?) • Max force, mN/mm (?) • Dip and look (qty pass/fail)

7. Data Analysis Methods • Analysis of Variance • To be used on the wetting balance data, where we have real measurements of performance • We want to determine if the different conditioning methods influence the measured solderability parameter • As a graph, we will use the box and whisker plot • Analysis of Means • To be used on the dip and look data where we only have pass/fail data • We want to determine if the different conditioning methods have an influence on the failure rate of the test samples • As a graph, we will use a means plot

8. Box Plots Maximum data value Box and whisker plots show both center and variability of thedata. Box covers the middle half of the data Median + Mean Minimum data value Unusual data value

9. Wetting BalanceGroup 4 – ANOVA Test An ANOVA tests whether there are significant differences among means. It compares the differences between means to variation within subgroups. In all these cases, the test indicates the differences are unlikely to be due to random causes (P<0.0000)

10. Wetting BalanceGroup 4 - Means Plot This plot show the mean value and the 95% confidence interval on the mean as calculated from the data

11. Wetting BalanceGroup 4 – T0 Measurement Problems On TO, we can observe a phenomenon often called “picket fencing” The measurement was recorded to two digits All measurements fell into one of three values Here, To becomes in effect a categorical variable, and techniques such a ANOVA cannot be used without bias

12. Wetting BalanceGroup 4 – Multiple Range Tests All distinguishably different For To, the groups fell into high medium and low bands For F2, all groups were different from one another The range test uses a multiple comparison procedure to determine which means are significantly different from which others. For Fmax, the groups fell into high and low bands

13. Wetting BalanceGroup 5 – ANOVA Test In all these cases, the test indicates the differences are unlikely to be due to random causes (P<0.0000) Again To shows big rounding problems

14. Wetting BalanceGroup 5 – Means Plots This plot show the mean value and the 95% confidence interval on the mean as calculated from the data

15. Wetting BalanceGroup 5 – Multiple Range Tests For T0: AR, D08 and W08 were similar. For F2, AR and D08 were similar. So were D08 and D16. D04 and W08 were similar. All others distinguishably different The range test uses a multiple comparison procedure to determine which means are significantly different from which others. For Fmax, the groups AR and D04 were similar, others were different.

16. Wetting BalanceGroup 6 – ANOVA In all these cases, the test indicates the differences are unlikely to be due to random causes (P<0.0000) Here, for the first time, T0 has taken on a range of values and lost the picket fencing problems.

17. Wetting BalanceGroup 6 – Means Plots This plot show the mean value and the 95% confidence interval on the mean as calculated from the data

18. Wetting BalanceGroup 6 – Multiple Range Tests For T0: AR and D04 are similar, as are D04 and D08. D16 and W08 are similar. For F2, only D08 and D16 are similar. The range test uses a multiple comparison procedure to determine which means are significantly different from which others. For Fmax, only D08 and D16 are similar.

19. Wetting BalanceSummary Lines connect conditions with roughly similar results

20. Dip and Look Test Results

21. Dip and LookGroup 4 – Analysis of Means The analysis of means tests the hypothesis that all the sample proportions are identical In this case, the test finds that it is unlikely (P=1%) that proportions like this could result from random chance alone Analysis of Means - Binomial Proportion Data variables: Pcnt Number of samples = 6 Average sample size = 9.66667 Mean proportion = 0.15431 Chi-Square Test ------------------------------------------ Chi-Square Df P-Value ------------------------------------------ 14.88 5 0.0109 ------------------------------------------ Warning: some cell counts < 5. The means plot shows which samples differ significantly from the grand mean Here, the results for the as received samples are quite different from the rest The other groups have results which are quite similar

22. Dip and LookGroup 5 – Analysis of Means The analysis of means tests the hypothesis that all the sample proportions are identical In this case, the test finds that it is highly likely (P=53%) that proportions like this could result from random chance alone, so these results are similar to one another Analysis of Means - Binomial Proportions Data variables: Pcnt Number of samples = 6 Sample size = 10.0 Mean proportion = 0.0333333 Chi-Square Test ------------------------------------------ Chi-Square Df P-Value ------------------------------------------ 4.14 5 0.5297 ------------------------------------------ Warning: some cell counts < 5. The means plot shows which samples differ significantly from the grand mean Here, all the sample proportions are similar to the grand mean and to one another The failure rates among the various sample subgroups are similar

23. Dip and LookGroup 6– Analysis of Means The analysis of means tests the hypothesis that all the sample proportions are identical In this case, the test finds that it is highly likely (P=48%) that proportions like this could result from random chance alone, so these results are similar to one another Analysis of Means - Binomial Proportions Data variables: Pcnt Number of samples = 6 Average sample size = 9.66667 Mean proportion = 0.120345 Chi-Square Test ------------------------------------------ Chi-Square Df P-Value ------------------------------------------ 4.51 5 0.4781 ------------------------------------------ Warning: some cell counts < 5. The means plot shows which samples differ significantly from the grand mean Here, all the sample proportions are similar to the grand mean and to one another The failure rates among the various sample subgroups are similar, dry conditioning for 4 hours is slightly higher but not enough be significant, given the number of samples we tested

24. Conclusions • When samples were conditioned and then subjected to solderability testing, the following observations were made: • Dip and look • Samples sizes are too small to see any trend in a pass/fail test • Test issues are the only items found (ex: 5 failures in an as received test) • Wetting balance • As received results are generally best • Conditioning at 72C/85RH for 8 hours is generally the most severe • The longer the dry conditioning the more the effect • 16 hour dry conditioning is often similar to 8 hours “wet” (72C/85RH) conditioning • Test questions can be seen in the data

25. Next Steps • The first step is complete, the pathfinder has achieved its goal, and validated • The parameter selection • The component selection • The test protocol • The second step is to continue the remainder of the designed experiment with the additional component types and surface finishes • The third step is to perform a confirmation run with more samples to gain greater statistical confidence