Category Archives: Dr. Ron

Materials expert Dr. Ron Lasky is a professor of engineering and senior lecturer at Dartmouth, and senior technologist at Indium Corp. He has a Ph.D. in materials science from Cornell University, and is a prolific author and lecturer, having published more than 40 papers. He received the SMTA Founders Award in 2003.

Alloy Density Calculator Generates Most Interest of All Blog Posts

Posted on by

Folks,

In the nearly 10 years that I have been blogging, I am continually surprised by the interest in a spreadsheet I created that calculates alloy densities.  I get about numerous inquiries a year on this topic.  We just renewed the link to the software, so I thought I would write a summary blog on its use and applicability.

First of all, the algorithm is intended for metals that form an alloy. Examples would be most solders and other metal systems where the metal atoms replace each other in the lattice. So, in addition to solders, copper and nickel would also work. The calculation assumes perfect mixing and that the sum of the initial volumes of the metals equals the total final volume. The correct formula for calculating densities is:

1/Da = x/D1 + y/D2 + z/D3

where

Da = density of final alloy

D1 = density of metal 1

x = mass fraction of metal 1

And the same for metals 2 and 3. 

This formula was derived in a past blog post. People are often surprised that the simple formula Da = xD1 + yD2 + zD3 is not correct.  The reason it is not correct is that density is inversely proportional to volume.  The error with this formula is discussed in another post.  The error, by using this formula, can be quite large. See the graph below for gold and copper. In some cases the error is more than 15%.

One example where the algorithm does not work would be for intermetallic compounds. The reason is that an intermetallic is a compound, not an alloy. Another example where the formula does not work is carbon in iron. The carbon atom is so small that it fits in between the iron atoms.

How accurate is the formula?  Work that I have performed with solder alloys suggests is it about 1-2% accurate. The accuracy can be affected by grain boundaries and the small amount of intermetallics that can form in some solder alloys.  An example is the small amount of  intermetallic “silver plate” (Ag3Sn) that can form in SAC alloys. I hope that many readers continue to find the density calculator useful.

Cheers,

Dr. Ron

Math musings. I read a fun book, The Joy of X. In the book, the author, Steven Strogatz, pointed out that the sum of consecutive odd numbers is always a perfect square. Try it: 1+3 = 4 = 2^2,  1+3+5 = 9 = 3^2 , 1+3+5+7 = 16 = 4^2, and so on.

Comparing Two Wiebull Distributions

Posted on by

Folks,

Let’s look at Patty’s last day of class …

As she was driving north to teach her statistics class, Patty was sad to see her stint at Ivy U come to a close. She was even more nervous about her meeting with Dean Howard after the class.

Before she knew it, she was standing in front of the class, to start her last lecture on Weibull analysis.

“Are there any questions before I begin?” Patty asked.

Patty nodded to Megan Ramsey.

“Professor, last time we talked about when a few samples don’t fail in a test that they are often censored in Weibull analysis. You mentioned that many people probably think it is good that some samples don’t fail. However, if the samples did fail at a later time it increases the scatter and would make the data worse. I’m not sure I understand that concept, as the scale has increased and the the top 10% of the samples would have a much longer life,” Megan summarized.

“Megan has an interesting point. Let me put both the censored (blue) and hypothetical data if the fails came later (red) on one graph. Discuss it for a while with those sitting next to you and see if you can conclude which data are better,” Patty suggested.

About three minutes went by and Patty called the class to order.

Megan was the first to raise her hand, which Patty acknowledged.

“After discussing it with Pete, (there were a few rolled eyes and soft whistles as everyone knew that Megan and Pete were an ‘item’) we concluded that the censored data (blue) is better. The most critical reason is that it predicts the smallest number of fails at a lowest number of cycles. We think this will always be the critical concern in reliability,” Megan answered.

“Precisely! This reason is why unfailed samples are not an endorsement to superior reliability. The censored data predict twice as many cycles – at a 5% failure rate. It is almost certainly misleading,” Patty said.

She chuckled a little then said, “If you want to impress someone in a job interview, discuss this topic.”

Patty didn’t know it, but one of the reasons the students like her as an instructor was her experience as an engineer. The many professors at Ivy U were brilliant, but few of them had actually been an engineer or managed a manufacturing process.

“OK, we have one last topic: how to tell if two Weibull distributions are statistically different,” Patty said.

“Let’s look at Weibull plots of stress test failures of alloys 5 and 6,” Patty said.

Prashant Patyl raised his hand.

“Yes, Prashant,” Patty acknowledged.

“Well. Alloy 6 (red) has a slightly higher scale and steeper slope, suggesting it is better, but it would be hard to say if it is statistically significantly better,” Prashant answered.

“Precisely,” Patty answered.

“Let’s try the plain old two sample t test,” Patty went on and showed a boxplot of the data.

The class chuckled a bit, as this test would be considered much more mundane than Weibull analysis.

“The t test shows that there is only a 30% confidence that the means are different. Just by visual inspection, the boxplot (below) suggests as much. So it would be hard to argue that the data are different at a 95% confidence level,” Patty elaborated.

Her comments resulted in much lively discussion about the normality of the data, if the mean a reasonable metric for comparison, and other perspectives and other related topics.

The ending of the class was very upbeat, so Patty was feeling an emotional high, until she remembered that she had to meet with Dean Howard. With trepidation, she headed toward his office. As she headed in, she was shaking a little.

“Professor Coleman, it’s great to see you,” Dean Howard said with enthusiasm and warmth.

Patty still couldn’t get used to being called “Professor,“ but she had checked on the Ivy U website and she was listed as a “Visiting Associate Professor.” They even had a webpage for her. She thought the photo they used made her look too old.

Before she could answer, Dean Howard got to the point.

“We have really been impressed with the teaching job you have done. The students were especially appreciative of your teaching style,” Dean Howard started.

“Thank you,” Patty said, her relief palpable.

“It appears that Professor Harlow, whom you are filling in for, will require a longer recovery than thought. In addition, we need a course on manufacturing processes. The bottom line is we want you to join the faculty to help us with these courses,” Dean Howard continued.

Patty nearly swooned.

“But sir, I don’t have a Ph.D.,” Patty responded.

“Our plan is that you have done such significant work at ACME, that you don’t need to do a thesis. We want you to take four courses while you teach. After successful completion of these courses, we will award you a Ph.D.,” Dean Howard went on.

Patty was so stunned she didn’t know what to say. She was silent for a while.

The Dean continued, “We can’t quite match your salary at ACME, but we can come close. I have already discussed the situation with Mike Madigan. He is supportive, but said the decision is obviously up to you. What do you think?”

Patty’s mind was spinning. Rob was getting his Ph.D. here, so that would help.

It was as if she was outside of her body looking and she saw herself say, “I would love to.”

They talked for 10 more minutes about some of the details and Patty relaxed a little. It occurred to her that she had not discussed it with Rob yet. Oh well. She expected that he would be supportive.

As they were wrapping things up, Dean Howard appeared to want to discuss a different topic.

After a few minutes of additional discussion, Patty left with a smile on her face.

Epilogue

Pete, as usual, always knew what was going on. He had never felt so depressed. He and Patty were a team. They had traveled all over the world solving electronics assembly problems and she was abandoning him to got to Ivy U! He was also nervous. He wasn’t that thrilled with the other people he thought likely to be his new boss. So, with head hanging, he shuffled toward Patty’s office.

“Hey, Pete! It’s great to see you!” Patty said cheerfully.

Pete got all choked up and didn’t know what to say. Finally, he mumbled with a shaky voice, “You’re leaving.”

“So are you!” Patty responded. “Assuming you want to be the Senior Research Associate for Manufacturing Processes at Ivy U.” They are even offering you 10% more than you make here – and the benefits are great,” she finished.

“Right after my offer, Dean Howard asked if I knew someone who could fill such a position, so I immediately suggested you. Apparently my endorsement was enough to land you the job, if you want it. Don’t screw it up,” she teased.

Patty, The Professor, and Pete in one location. Only time will tell what new adventures await.

Cheers,

Dr. Ron

 

Weibull Analysis at Ivy U

Posted on by

Folks,

Let’s check in on how Patty is doing at Ivy U.

Patty was nearing the end of her teaching stint at Ivy U. Only a few more classes remained. She had to admit that she was sad to see this adventure end. Oh, well, such is life.

The syllabus allowed for the last few lectures to cover “Selected Topics,” so Patty decided that her selected topics would be Weibull Analysis. She felt passionately that all engineers should have some exposure to failure analysis and this topic fit right in to engineering statistics.

Before she knew it, she was heading north up to Ivy U for her next-to-the-last lecture. She thought she should soak in the beauty of the campus as her car approached, for soon this would be her last time at Ivy U for a while. Today she was lucky, she found a parking spot right away.

As she walked into the main engineering building, she noticed a note in her mailbox. It was from Dean Howard. She quickly opened it. He was requesting a brief meeting after her last class.

“Yikes!” thought Patty, “Dean Howard wants to see me! I wonder if it’s serious. Did I goof up, somehow?”

She would have to wait for two days to find out what the Dean wanted and she couldn’t worry about it now, as her class was starting in 10 minutes.

Patty began the class by explaining the development of Weibull’s theory and gave a few examples. She showed where the scale factor and slope came from. Patty emphasized that a steep slope indicted a tight distribution of data (a good thing for prediction from the data) and that a larger scale suggested a longer mean life. She then discussed the importance of different types of tests in electronics, such as thermal cycle testing and drop shock testing. As an example, she thought she would share some accelerated thermal cycle data for two different alloys that are used in electronics assembly.

She showed the first set of data in a PowerPoint slide (Figure 1).

“Can someone explain these results to me?” Patty asked.

After some murmuring, Karen Armstrong raised her hand.

“Yes, Karen,” Patty responded.

“It appears that Alloy 2 demonstrated superior performance, as seen in its much steeper slope and slightly better scale,” Karen answered.

“Nice job, Karen,” Patty responded.

“What about this one point?” Patty asked as she pointed to the obvious outlier for Alloy 1.

There was more murmuring, but no one raise their hand. So Patty showed a slide with the outlier removed.

“I have removed the outlier because failure analysis showed it was atypical,” Patty said.

“As you can see, now alloy 1 has a slightly better slope. This suggested a tighter distribution and hence more ability to predict performance,” she went on.

There was now very loud murmuring, finally Scott Bryzinski raised his hand.

“Yes, Scott?” Patty responded.

“Professor, it just seems like cheating, dropping a bad data point because you claim it is not representative of the other samples,” Scott explained.

There were many loud echoes of agreement.

Patty chuckled a little.

“OK, OK, you are right. It is not fair to censor a data point in most cases. This is part of the lesson of this class. Don’t censor data lightly,” Patty said.

“Let’s look at data for Alloy 3 and 4,” Patty went on.

The students looked at the data for some time and finally Diane Pompey raised her hand.

“Yes, Diane,” Patty acknowledged.

“They look about as dead even as one could expect, except that the sample sizes are different. Alloy 3 has 15 samples and Alloy 4 only 13 samples, as can be seen in column ‘F’ in the ‘Table of Statistics’,” Diane explained.

“Nice work Diane, few people would have picked up on that difference,” Patty replied.

“I will tell you that both alloy 3 and 4 had 15 samples to start with in the test. What do you think happened?” asked Patty.

Very quickly, Fred Wilkins raised his hand. Patty nodded to him.

“I’ll bet that two of the samples from alloy 4 did not fail,” Fred suggested.

“Correct!” Patty responded enthusiastically.

“I want you all to take a few minutes to discuss this situation with those seated around you. I then want you to vote anonymously whether the two samples that did not fail make alloy 4 the same, better or worse than alloy 3,” Patty instructed.

After five minutes of noisy discourse, the students voted on a website, the results of which Patty could show on her laptop and project to the class. Twelve students thought the alloys were still the same. 24 thought alloy 4 was better, and 6 thought alloy 4 was worse.

“Any comments on the results?” Patty asked.

There were no takers.

“Let’s assume that the two samples that failed were tested for a much longer time and they finally failed at some very high number of cycles, say 2,000. Let’s look at what the Weibull plot would look like,” Patty said.

She then showed Figure 4.

“Can anyone explain it?” Patty asked.

After a short time, Young Koh raided his hand.

“Dr. Coleman, the added cycles increased the scale significantly, but ruined the slope, suggesting much more scatter in the data. As you suggested earlier, reliability testing is about hoping to have the ability to predict lifetime. With the large decrease in the slope, prediction becomes much more difficult, So, sample 4 is likely worse than sample 3, even though it has a large scale.” Young expounded.

“Precisely,” Patty answered.

“It is interesting to note that many engineers in the electronics industry today just ignore the samples that don’t fail,” Patty went on.

The class looked at her with shocked faces.

“Well, that’s all until next time,” Patty said.

“Two of the female students, Jessica Han and Mary Connor, stayed after the class to talk to Patty.

“Professor, there is a rumor that you will be teaching “Manufacutring Processes” next term, is it true?” Mary asked. Then went on, “We really hope so. You are best teacher here.”

Patty was so touched she started getting a little misty eyed, “Thank you for your kind comment, but I doubt that that will be the case,” she said as her voice quavered.

Will the Dean fire Patty or will she be teaching Manufacturing Processes the next term. Stay tuned to see.

Cheers,

Dr. Ron

 

‘Patty’ in the Real World

Posted on by

Folks,

Every year, the wonderful folks at PCM host a visit from my class on manufacturing processes and provide a real-world tour of an electronics assembly facility. Our relationship has resulted in the class producing a video on electronics assembly. In addition, several class projects have been performed at PCM over the years; projects that have helped my students learn and have, hopefully, helped PCM’s operation.

A few weeks ago it was time for this year’s student visit. Rob Steele and Jon Scheiner were our hosts. During the tour, Rob mentioned that he and the PCM team have implemented many of the productivity concepts discussed in The Adventures of Patty and the Professor. Rob even mentioned that he thought the book, at some level, was a “page turner.” It is personally rewarding to see people benefiting from this book.

Anyway it is very clear from our tour that productivity is high at PCM. It is my hope that others might also benefit from the stories in The Adventures of Patty and the Professor. If you have benefited from the book, please let me know.

Cheers,

Dr. Ron

 

Sample Size is Important in Weibull Analysis Too

Posted on by

Some time ago I posted on “The Curse of the Early First Fail” and “Interpreting Weibull Plots.” Both of these posts related to using Weibull analysis to make sound engineering decisions.

Recently, a reader asked if sample size is important in Weibull analysis. It is interesting that few who do Weibull analyses discuss the effect of sample size. So, let’s do it now. Consider Figure 1. This figure shows Weibull analysis used to compare cycles to fail for Alloy 1 and Alloy 2. Considering that the slope of each curve is about the same, most people would say that since the scale for Alloy 2 is greater (1320 versus 1172), Alloy 2 is superior. But, is the difference statistically significant? By using a simple Two Sample t Test, we can analyze the data and find that there is only a 62% confidence that Alloy 2 is better than Allot 1. Flipping a coin gives us 50% confidence, so this result is not encouraging. Four samples is seldom enough to make a confident engineering decision.

Figure 1. A Weibull plot of Alloy 1 and 2 with only four samples.

If we perform the experiment again with 20 samples, we get the Weibull analysis as shown in Figure 2. Note that although the scale parameters have not changed too much, the shape parameters have changed significantly. The original 4 sample test is just not enough to really lock in on the real shape numbers for the samples. By also performing a two sample t test on the 20 sample data, we now find we have a 99.6% confidence that Alloy 2 is superior to Alloy 1. So, with 20 samples we can confidently say that Alloy 2 is superior to Alloy 1.

 

Figure 2. A Weibull plot of alloys 1 and 2 with 20 samples.

What is the minimum sample size for your test to be confident in the result? It can vary quite a bit and only by analyzing the data with a t test, after the experiment, can you know for sure. But my experience would suggest that you should never have less than 10 samples, and preferably 15 or more.

Cheers,

Dr. Ron

 

An Example of Cpk and Non Normal Data In Electronics Assembly Soldering

Posted on by

Folks,

Let’s see how Patty is doing after teaching her first class at Ivy U…

Patty arrived at home after teaching her first class at Ivy U and she couldn’t contain her excitement. For the next couple of hours her husband, Rob, had to politely listen to her talk about how amazing it was to teach the young, bright, enthusiastic, future engineers.

Time went quickly and, before she knew it, she was standing in front of the class for her second lecture.

Patty reviewed quickly the fact that it was incorrect to average Cpks and that, to calculate a Cpk, the data should be normally distributed.

“The question was asked last time, how one can tell if the data are normal? Minitab can be used to plot the data on a normal probability plot. By eye, we can get a good sense if the data are normal or not. In addition, Minitab will perform various tests, one of them being the Anderson-Darling Normality Test,” Patty began.

“Let me show you some real data to demonstrate this,” Patty continued.

“When assembling a smartphone like the new Druids, the mechanical tolerances for the many tiny capacitors and resistors are very precise. One common capacitor size is only 0.6mm long.” Patty paused as she saw a student’s hand raised.

“Yes, Martin?” Patty asked.

“Professor, you mean 0.6cm, right?” Martin asked.

“No, 0.6mm,” Patty answered.

Patty’s answer caused quite a bit of murmuring, finally Patty had to ask for order.

“Why is everyone surprised?” Patty asked.

“Professor, that is much smaller than a grain of rice, it is more like the size of a grain of sand,” Alison March responded.

“This is fun,” Patty thought, “and good timing.”

Patty showed a slide of passives on a match head and passed around a teardown of Druid smartphone with a magnifying glass so that the students could see how small the passives were.

It took a while for the class to calm down.

Patty then said, “And about 200 to 500 capacitors and resistors of this size are individually placed and soldered in the electronics assembly process in each smartphone.”

The student’s mouths were agape.

“OK, let’s discuss how these little rascals relate to SPC. My company orders billions of these electrical components each year. We are sent a sample lot to approve a larger order. For the components of interest, we already know that the mean length is 0.6mm, the 3 sigma (standard deviation) tolerance is +/-0.03mm. So, the lower spec limit is 0.57mm and the upper spec limit is 0.63mm. This equates to a Cpk = 1.00,” Patty went on.

Patty put a PowerPoint slide up that showed the data.

“The data for the sample lot is on the left. What is the problem?” Patty asked.

Charles Parsons raised his hand.

“Yes, Charles?” Patty asked.

“Well, Dr. Coleman, the standard deviation for the data is 0.15 and the resulting Cpk is only 0.67, so the targets are not met” Charles answered.

“Precisely,” Patty replied.

“We then went back to the supplier and asked them to fix the problem. The graph on the right is from the capacitors they sent us 3 weeks later, after they claimed to have solved their manufacturing process problems,” Patty explained.

“What do you think?” Patty asked.

There was a lot of murmuring. Finally DeShaun Martin raised his hand.

“Yes, DeShaun?” Patty acknowledged him.

“Well, Professor, it looks like they simply sorted out the parts to make the sigma lower and Cpk higher,” DeShaun responded.

“Precisely,” Patty said.

“I don’t see what is wrong with sorting,” Sandy Lisle commented.

“You see from the Druid smartphone that I passed around that it is so densely packaged with components that there is hardly any room in it. To achieve this density we have to perform tolerance analyses to assure everything will fit. In all of these analyses we model with normal distributions. With a sorted distribution we will likely have more tolerance interferences,” Patty answered.

“Look at the red arrow. There will be an excess of components with this size and a lack of components of the size where the green arrow is pointing. These differences will cause some tolerance interferences with the pads on the printed wiring board where the passive will be assembled,” Patty continued.

“Can you review? How we can tell that the distribution on the right is not normal?” Conor Stark asked.

“Sorry, I almost forgot. Look at the normal probability plot for the sorted data. Note how it diverges from the straight line on the ends. Also the Anderson-Darling value for p is <0.05. These two criteria are cause to reject the hypothesis that the data are normal,” Patty finished.

Patty was just wrapping the class up when someone raised their hand.

“Yes, Natalie?” Patty asked.

“What was the final outcome?” Natalie asked.

Patty chuckled, obviously she should share he result of this adventure.

“Oh, yes. We could not use the parts for the Druid smartphones, but they were OK for some toys we were assembling. In addition we insisted on a 30% discount, since the passives did not meet the specification,” Patty answered.

As she was cleaning up, two of the female students came up to Patty. One them, Justine Randall spoke for the two.

“Professor Coleman, you are an inspiration for us. We hope in 20 years we can be just like you,” Justine said with emotion.

Patty was indeed touched, but as she left the classroom, she decided that she had to start dyeing her hair.

Cheers,

Dr. Ron

 

Cpk Can Only be Calculated from Data that are Normally Distributed

Posted on by

Folks,

Let’s look in on Patty….

Patty was really nervous. As a matter of fact, there was no time she remembered being this nervous. The cause of her nervousness? She was going to teach a series of classes at Ivy University.

This opportunity came about because one of the professors at IU was in a serious accident. A full recovery was expected, but there was no way the prof could finish the last three weeks of the statistics course she was teaching. Things are hopping in the Engineering Department at IU, and the Dean could not find another prof to take over.

The Professor himself was too busy, however, when the Dean asked his advice he immediately recommended Patty. Patty was honored by the request and would be more so if she knew the behind-the-scenes story. IU has an unwritten rule that all teaching profs had to have a Ph.D., which Patty did not. However, if Bill Gates wanted to teach, an exception would be made, as he is a world class technical executive. Patty was hired under that exception. She was stunned to see she was on the front page of “The Ivy U Review,” under the headline, “Famed Executive to Teach at IU.”

Well, her first class was tomorrow and she took comfort in the fact that her husband, Rob, told her she shouldn’t be nervous. Pete wasn’t much help. He told her he would be so nervous that he wouldn’t be able to eat. It was just unnerving teaching the best and the brightest. She was proud of her academic accomplishments at Tech, but this was IU, arguably one of the top 10 universities in the country.

“Rob, I have to tell you, even though I’m still nervous, it comforts me to know that you wouldn’t be nervous,” she said to her husband.

“I never said I wouldn’t be nervous, I said you shouldn’t be nervous. After all, you’re Patty Coleman,” Rob replied.

At this Patty burst into tears and Rob came over and gave her a big hug.

“But, you’re smarter than me,” Patty insisted.

“No way,” Rob replied.

They then spent the next 10 minutes arguing that the other was smarter. Patty always felt she had a good business sense, but for understanding deep technical things, she believed Rob was her superior. After a while they looked at each other and laughed.

“Not too many couples would get in to an argument, saying that the other person was smarter,” Rob teased.

Time passed quickly and Patty was soon in front of the 35 students in the class. The topic was Cpk as the most important metric to determine the quality of a lot of material or product. She asked the students if it was OK to average Cpks from different lots. A student raised her hand.

“Yes Emily.” said Patty. (Patty had ask the students to use nametags.)

“No, Professor Coleman. One can’t average Cpks. The reason being that Cpk goes as one over the standard deviation and standard deviation is a squared term. So one can’t average two lots and get the same result as taking the Cpk of all of the two lot data.

Patty responded, “Emily is 100% correct. Remember, when you get out in the working world, to always check to see that your suppliers are not averaging Cpks. This might happen when half of the lot is below the spec, say the Cpk is 1.4 and the spec is 1.5 and the other half is over the spec, say 1.7. The supplier will say that the lot is in spec because the average Cpk is 1.55. This isn’t necessarily so, as Emily points out. You should only accept a calculation of Cpk for the entire lot.”

Patty chuckled that Emily thought she was a Professor, at Ivy U. Right!

The class continued to go well and Patty began to relax. As the class began to end, she mentioned another important point.

“What important criteria must the data have to be considered acceptable to calculate Cpk?” Patty asked.

There was a bit of murmuring and finally a boy (man?) raised his hand. He looked 12 years old to Patty.

“Yes, William,” Patty acknowledged him.

“Dr. Coleman, I think the data must be normal,” William answered.

“Dr. Coleman?” Patty thought.

“Absolutely correct, William,” Patty responded.

“Class, remember this point when you get out into industry. Almost no one checks to see that the data are normal before calculating Cpk. The data must be normal to calculate Cpk. I can’t tell you how many times I have rejected a lot of incoming material because the Cpk was calculated from non-normal data. In some cases non-normal data can be transformed so that the data are normal, “ Patty continued.

“Professor, how do we know if the data are normal?” a student named Kathy asked.

“Stay tuned for the next lecture,” Patty chuckled and dismissed the class.

As she was gathering her laser pointer, lap top etc., a number of students came to talk to her. Emily was with a group of about six of them.

“Professor, we just wanted to tell you that we are thrilled to have you as our instructor. We appreciate your practical, real world perspective on statistics,” Emily said.

Patty responded warmly and was close to being choked up by this show of respect and appreciation. She decided she would walk to The Professor’s office to tell him how it went.

On the way out, she heard one of the male students say to his friend, “You know, she is quite attractive for an older woman.”

Patty didn’t know whether to laugh or cry.

Cheers,

Dr. Ron

My BBC Interview on Tin Pest

Posted on by

Imagine my excitement when Laurence Knight of the BBC contacted me to see if I was interested in being interviewed on the topic of tin pest, with a secondary discussion on tin whiskers. After a 30-minute phone call, it appeared that I passed muster, as I was asked to come to their studio to have a formal interview. Immediately, visions of visiting London crowded my mind. I haven’t been to London in a while and I would like to see the Tower of London and the London Science Museum again. Suddenly, a dreadful thought sweep over me, they probably have a studio in the US, perhaps Boston. So, in my mind, I quickly settled on a visit to the Boston Science Museum and the Isabella Stewart Gardner Museum to see if there is any news on the fate of the art treasures stolen 25 years ago. Rembrandt’s “The Storm on the Sea of Galilee” was one of the paintings stolen in the 1990 robbery at the Isabella Stewart Gardner Museum in Boston.

Even this meager plan was soon dashed, when Laurence informed me that they could likely use a PBS studio in Vermont or New Hampshire. I actually ended up in a radio studio at Dartmouth. Somehow by using an ISDN telephone line and recording at Dartmouth and in the UK they can achieve acceptable fidelity.

From the outset, I wanted my message to be:

  1. The reliability concerns for tin whiskers are well founded, however there are many mitigation and design techniques that can reduce tin whisker risk.
  2. Tin pest is much rarer than tin whiskers, however there appears to be little effort in mitigating tin pest at all. This lack of attention may cause some tin pest failures in cold environments.
  3. Interestingly, the mitigation for tin pest (2% bismuth or 0.5% antimony) also dramatically suppresses tin whiskers.

So, on January 20, I was interviewed in the Dartmouth radio station (alas only ¼ of a mile from my office) for 20 minutes. The broadcast occurred on January 29. I didn’t know that my interview was part of a much larger story on tin metal. The 20 minutes I spoke was pared down to just a few. I let you decided if the trimmed version got the message across I intended. I am speaking about 35% through the audio file.

Cheers,

Dr Ron

The Importance of Oxygen Barrier in Solder Pastes

Posted on by

Folks,

Pity the solder scientists of the late 1970s and early 1980s. SMT was an emerging technology and the world wanted to buy solder paste. However, the only experience many solder scientists had was wave soldering. In wave soldering, the flux’s main job is to remove the oxides from the PWB pads and components. The solder is in a molten state and its oxidation is not a main concern. In the soldering process, the solder only touches the board for a few seconds and the board only experiences high temperatures during this brief period.

I imagine some early solder pastes consisted of solder powder with fluxes similar to those used in wave soldering. If so, they probably didn’t work too well. Consider the dramatic differences that solder paste experiences as compared to solder in wave soldering. The “flux” in solder paste has to remove oxides from the PWB pads, component leads and solder particles, but it also has to protect all of these surfaces from re-oxidation for several minutes in the reflow oven. To achieve this protection, the “flux” has to contain materials that act as oxygen barriers. The most common oxygen barrier materials used in no-clean solder pastes are rosins/resins. Rosins, or resins, which are modified or synthetic rosins, are generally medium- to high-molecular weight organic compounds of 80-90% abietic acid. They are typically found in coniferous trees. Rosins/resins are tacky in nature, they provide some fluxing activity, and provide the critical oxidation resistance during the reflow process.

The reason I wrote “flux” in quotation marks in the above paragraph is that what most people call the flux in solder paste is actually a complex combination of materials. These “fluxes” consist of:

  • Rosins/resins: for oxygen barrier and some fluxing activity
  • Rheological additives: to give the best printing properties. e.g. good response to pause, good transfer efficiency, excellent slump resistance, good tack, etc
  • Solvents: to dissolve the other materials
  • Activators: to perform the main fluxing action (removing oxides).

Because of these complexities, and the material’s multi-functionality, they are sometimes referred to as, “flux-vehicles.”

Modern solder pastes must have good oxygen barrier capability. In most reflow profiles, the solder paste is at temperatures above 150°C for several minutes. During this time an oxygen barrier is needed to protect both the solder particles and the surfaces of the pads and leads.

The graping defect. A common example of cases where the solder barrier was insufficient is seen in the graping defect, or its relative, the head-in-pillow defect. If you are experiencing one of these defects, a solder paste with better oxygen barrier properties is bound to help.

Cheers,

Dr. Ron

Lack of Concern for Tin Pest as a Reliability Issue in Mission Critical Products Still Hard to Understand

Posted on by

Folks,

My recent post on tin whiskers sparked the memory of tin pest in my mind. I have to admit, that with all of the legitimate reliability concerns related to tin whiskers, I am surprised that there has been essentially no parallel concern for the risks of tin pest.

Admittedly, tin pest is much more rare than tin whiskers. Although many complain that we don’t understand tin whiskers, we can create them easily and make the vast majority of them go away. Whereas, it has been shown to be very difficult to create tin pest.

For those who want a refresher on tin pest see this blog posting or my survey paper “Tin Pest: Elusive Threat in Lead-Free Soldering?” Journal of Failure Analysis and Prevention, vol. 10, no. 6, December 2010 , pp. 437-443(7).

Tin pest is a result of an allotropic transformation of tin from its beta phase (white or normal tin) to its alpha phase (gray tin) at temperatures below 13oC. This transformation is accompanied by a change in density from 7.31 g/cm2 to 5.77 g/cm2. The reduction in density requires the tin to expand, thus destroying the structure of the original tin object or solder joint as seen in the figure below.

With tin pest being so rare, why am I concerned with it as a reliability exposure? With billions of solder joints in mission critical circuit boards exposed to cold for many years, it would seem inevitable that some tin pest would form. The effect of the cold is cumulative, it does not get reversed when the weather becomes warm. Applications most at risk would be automobiles, mobile phone towers, and military equipment.

I wouldn’t be surprised that, with typical tin whisker mitigation, that unmitigated tin pest might be more common.

What is the fix? By adding about 0.5% antimony or 2% bismuth to lead-free solder, tin pest can be essentially eliminated. An added blessing would be suppression of tin whisker formation also. However, adding even these small amounts of antimony or bismuth to lead-free solders would require a thorough evaluation. Even these small additions of alloying elements can dramatically change the properties of a solder.

Best Wishes,

Dr. Ron