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.

The Professor, Part IV: The Price of Changeovers

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So far the meeting with The Professor had proven very valuable John thought. He was anxious to hear the other suggestions The Professor had.

The Professor began to speak. “Changeovers are what really hurts ACME’s uptime and hence productivity.”

Pete was surprised. “Even you were impressed with our system of having a white board to document logistics status for each future job.”

“You are correct,” responded The Professor. “However, a changeover takes you about 2-3 hours and you have one or two changeovers per line per day.”

“We have a high product mix business; it’s what we do,” said John.

“The good news is, you can cut your changeover time to 30 minutes,” shared The Professor.

“How?” asked John.

“By using feeder racks,” explained The Professor. “These racks allow you to set up component reels for the next job while the current job is running. Admittedly they cost about $30,000, but they will pay for themselves in weeks. Right now you lose more than two hours per changeover loading feeders onto component placement machines. With the feeder racks, you just roll them and lock them in place.”

Pete moaned, “We already have feeder racks. We only used them once, because they stick on the carpet when we move them.”

This comment caused The Professor to groan internally, but he hid it well. He had noticed the frayed carpet near the component placement machines.

John was beside himself. “It’s a good thing we are not The Professor’s students … I don’t think we would be headed for an ‘A,’ “ he thought. “Pete, let’s get facilities to remove that rug and start using the feeder racks ASAP.”

Patty listened to all of this with comical fascination. She had harassed Pete about using the feeder racks several times. While the meeting was going on she drew a sketch of The Professor, who is notoriously camera shy. Oh, and she decided on the restaurant, Olives in nearby Boston. Maybe they can pick up a Red Sox game while they’re there.

Epilogue: Six months later ACME’s uptime was a respectable 30.4%. John never had to buy another line. The improved productivity enabled ACME to increase its market share. Patty’s dinner and ballgame were a complete success. She handled her victory modestly and she and Pete became best friends. Pete also joined the ranks of The Professor’s admirers.

Dr. Ron’s note: I know that a story like this seems too comical to be true. However, every point and the associated uptime numbers, lost time, etc., is based on a real situation with no exaggeration. The names have been changed to protect the innocent (guilty?) What is your uptime?

The Professor’s 2d Visit, Continued

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“Well, what should we do Professor?” John said weakly.

“Clearly, not shut the line down over the lunch hour,” The Professor responded quickly.

“We can’t,” said John. “The operators are all friends and they count on having lunch together.”

“How much are they paid per hour?” asked The Professor.

“Ten dollars,” replied John.

“You can pay them $15 per hour and still make more profit if they keep the line running over the lunch hour,” The Professor opined.

“Fifteen dollars per hour for the lunch time or the 40 hour week?” John asked nervously.

“For the whole week,” was The Professor’s reply.

“I find that hard to believe,” John shot back.

“Consider this,” said The Professor.” Your line is up only 9.7% of an eight-hour shift; that’s only 47 minutes. Today you lost 95 minutes over the lunch hour. You may be able to increase your uptime to greater than 15% by keeping the line running over lunch. I modeled your business with ProfitPro3.0 cost estimating software, and your company will make millions more per year if you keep the lines running over lunch. I have worked with other companies to make this change, and it is really easy with a 30-minute lunch hour. If five people normally run the line, you have just one stay back over lunch hour; that way, each person only misses the lunch hour once a week.”

John thought optimistically, “There is such a thing as a free lunch.”

“Now let’s talk about what we can do to double the uptime from the 15% we will get by running the lines over lunch,” said the Professor.

Patty listened to all of this in amazement. The Professor was helping ACME more than she thought possible. Yes, John will keep his job. What is The Professor’s plan to get uptime to 30% or more? Where Patty will go to dinner?

Stay tuned for the latest.

Cheers, Dr. Ron

Dr. Ron note: As surprising as this may seem, this story is based on real events. The uptime numbers and improvements are from real examples. A company that can acheive 35% or more uptime can compete with anyone in the world, even in low labor rate countries. Sadly, few companies know their uptime — or have the urgency to improve it.

Understanding Uptime

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Two weeks passed quickly and The Professor returned to ACME. Patty met him at the door.

“Professor, it’s great to see you,” Patty said with enthusiasm. “We collected the uptime data in real time on a laptop; no one has seen that results yet. We wanted it to be a surprise,” said Patty.

The Professor suggested that he go out on the shop floor to observe the manufacturing activities until shortly after lunch. He commented that his observations may help to understand the uptime results.

The morning seemed to drag for Patty, as she was anxious to see the resets of the uptime data. She bet Pete that the uptime would not be more than 50%. If she wins, Pete and his wife will treat her and her boyfriend Jason to dinner.

Around 1:30 pm The Professor suggested that he was ready for the meeting. Patty had written a simple Excel macro to perform the calculations for the uptime. She only had to push a button and he whole room would see the result in a moment, as Patty had connected her laptop to a projector. There was tension in the air, friendly wagers had been made, but the entire process team realized that their reputation was on the line.

When the number emerged on the screen, John, the manager’s face became ashen. Pete’s visage was redder than two weeks ago. John thought, “I should be fired. How could I manage this team for 5 years and not know that our uptime was only 9.7%.” Patty was thinking about her choice of restaurants.

John asked The Professor, “How can we be so bad?”

The Professor responded, “The good news is that there are tremendous opportunities for improvement. After observing the operations out on the floor this morning, I think we can get the uptime to greater than 40%.”

Pete shot back, “You’re kidding, only 40%?”

“I’ve only seen two facilities that have greater than 45% uptime, and I’ve been to over 150 facilities worldwide,” answered The Professor.

“Where do we start?” asked John.

“How about lunch?” beamed The Professor.

“We just had lunch!” Pete groaned.

“No, no Pete,” The Professor chuckled, “I mean how lunch is handled out on the line. Lunch costs the company more than 1.5 hours of production in an eight-hour shift. That’s nearly 20% of the entire shift.”

Now John was a little agitated. “Professor, lunch is only 30 minutes. We purposely have a short lunch period to avoid the line being down for a long time,” John said with a note of annoyance.

“John, this is true, but I watched what the operators did. Lunch is supposed to start at 12 noon, but the operators turn the line off at 11:40 am. They don’t get back to the line until 12: 40 and it takes them more than 30 minutes to get the line running again. Today the line was not running until 1:15 pm. It was down for 1 hour and 35 minutes,” stated The Professor.

John thought again, “Yes, I should really be fired.”

Will John keep his job? What restaurant will Patty choose for dinner? What should be done about lunch? Where are all of the other hours lost? Stay tuned for the answers to these and other questions.

Line Balancing: The Professor’s Tale

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Folks,

Business was good at ACME.  Even in these challenging times, the company’s three assembly lines could not keep up with demand.  John, the manger of the assembly lines, decided to request the funds for an additional assembly line.  A member of his team, Patty, suggested he might want to consult “The Professor,”*before getting a new line.

The Professor taught a course on line balancing that Patty took at the SMTAI conference last summer.  Line balancing is an important part of optimizing productivity in electronics assembly. A balanced line ensures that the component placement process, usually the “constraint,” is the fastest possible by ensuring that each placement machine spends the same amount of time placing components.  If any machine is waiting for the others, assembly time is being wasted.  In a sense line balancing is an application of Goldratt’s Theory of Constraints. John remembered that when Patty applied what she learned from The Professor, throughput increased 25%.  Unfortunately, Patty did not attend The Professor’s other class on “Increasing Line Uptime.”

John decided to have a chat with Patty about The Professor.

“Patty, why do you think I should consult with The Professor, about getting a new line?”

“Well John, perhaps with some effort to improve our uptime , we wouldn’t have to buy another line,” said Patty.

“That’s a good point,” replied John.

Patty contacted The Professor and he agreed to fit ACME into his busy schedule. Upon his arrival, The Professor was given a tour. As part of the tour he was shown the process that ACME used to minimize changeover time between jobs. The Professor appeared impressed. After the tour, The Professor asked if a brief meeting could be held with the engineers and managers to discuss the situation.

“What is the average line uptime?” The Professor asked the assemblage.

There was some hemming and hawing, finally Pete, the senior process engineer replied, “I’d say at least 95%, we work our fannies off out there.” There was a murmur of agreement from the 9 or 10 people in the room.

Finally John spoke up, “Professor, what is your definition of uptime?”

The Professor responded, “Simply the percent of time an assembly line is running.” Pete again responded that 95% was the right number.

The Professor asked for some production metrics and performed some calculations on his laptop. In a few moments he commented, “From the data you gave me, I estimate that your average line uptime is about 10%.”

Upon hearing this, Pete became red in the face, especially after Patty whispered in his ear, “I told you so.” The noise in the room became so loud that John was concerned he might have a riot on his hands. The Professor asked to speak and John, in a booming voice, asked for calm.

“Let’s not become angry, perhaps my calculations are off. Why don’t we measure the uptime for a few weeks to be certain.”

“How do we do that?” asked Pete, his face still crimson.

“Each day one process engineer will go out to the lines every 30 minutes. If the line is running, he will put a 1 in an Excel spreadsheet cell. If the line is not running, a 0 will be entered,” responded the professor.”

It was agreed that this will be done and The Professor would come back in two weeks.

Will Pete’s red face return to normal? Will the line uptime be 95%? Will Patty and Pete ever be on speaking terms again?  Stay tuned for the next episode.

Cheers,

Dr. Ron

*The Professor, as he is affectionately called by his many students, is a kindly older man who works at a famous university. Few know his real name. The Professor is an expert in process optimization.

Calculating Solder Alloy Density

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Folks,

I continue to get much interest in the solder alloy density calculator I developed. It is now online here. It assumes no chemical interaction between the metals and no formation of interstitials. It works well for solder alloys.

Many people have an incorrect idea of how to perform this calculation. The most common incorrect concept is to multiply the % by weight of each alloy times its density and add them together. Using this incorrect approach one would calculate the density of tin-lead eutectic solder as 8.79 g/cc (0.63 x 7.29 + 0.37 x 11.34) vs the correct 8.4 g/cc. The correct derivation follows.

We want to find the density of an alloy composed of three metals. Assume the mass of the alloy is M. Metal A has a mass ma and a density da, Metal B has a mass mb and a density db and Metal C has a mass mc and a density dc. The total volume, V, of the three metals is va + vb+ vc.

However, since v = m/d, the total volume can be expressed:

V = ma/da + mb/db +mc/dc (Eq. 1)

The density of the resulting alloy is D = M/V, hence 1/D = V/M, therefore:

1/D = V/M = (ma/M)/da + (mb/M)/db +(mc/M)/dc (Eq. 2)

Now ma/M is the mass fraction of a, which we will call Xa, and similarly Xb and Xc for metals B and C.

Eq. 2 then becomes:

1/D = Xa/da + Xb/db +Xc/dc

which is our solution.

This principle also can be applied to alloys of more than three metals.

Tin Pest: A Forgotten Issue in Pb-Free Assembly?

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Tin is a metal that is allotropic, meaning that it has different crystal structures under varying conditions of temperature and pressure. Tin has two allotropic forms. “Normal” or white beta tin has a stable tetragonal crystal structure with a density of 7.31g/cm3. Upon cooling below about 13.2°C, beta tin turns extremely slowly into alpha tin. “Gray” or alpha tin has a cubic structure and a density of only 5.77g/cm3. Alpha tin is also a semiconductor, not a metal. The expansion of tin from white to gray causes most tin objects to crumble.

The macro conversion of white to gray tin takes on the order of 18 months. The photo, likely the most famous modern photograph of tin pest, shows the phenomenon quite clearly.
39-40.

This phenomenon has been known for centuries and there are many interesting, probably apocryphal, stories about tin pest. Perhaps the most famous is of the tin buttons on Napoleon’s soldiers’ coats disintegrating while on their retreat from Moscow. Since tin pest looks like the tin has become diseased, many in the middle-ages attributed it to Satan as many tin organ pipes in Northern European churches fell victim to the effect.

Initially, tin pest was called “tin disease” or “tin plague”. I believe that the name “tin pest” came from the German translation for the word “plague” (i.e., in German plague is “pest”).

To most people with a little knowledge of materials, the conversion of beta to alpha tin at colder temperatures seems counter intuitive. Usually materials shrink at colder temperatures, not expand. Although it appears that the mechanism is not completely understood, it is likely due to gray alpha tin having lower entropy than white beta tin. With the removal of heat at the lower temperatures a lower entropy state would likely be more stable.

Since the conversion to grey tin requires expansion, the tin pest will usually nucleate at an edge, corner, or surface. The nucleation can take 10s of months, but once it starts, the conversion can be rapid, causing structural failure within months.

Although tin pest can form at <13.2°C, most researchers believe that the kinetics are very sluggish at this temperature. There seems to be general agreement in the literature that the maximum rate of tin pest formation occurs at -30° to -40°C. How much of a worry is tin pest in practice? Probably not too much. Small amounts (0.01 to 0.1%) of some metals, most notably antimony and bismuth, inhibit the formation of tin pest, probably by solid solution strengthening. Because most tin will have such impurities, researchers have actually found it hard to produce tin pest in the lab. A concern, of course, is that these impurities are uncontrolled, leaving open the chance of tin pest showing up in some cold temperature applications. I have written a paper that discusses tin pest in more detail. If you are interested, send me a note and I will send it to you.

Thoughts on Rheology, by Dr. Andy Mackie

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Folks,

Indium Corp.’s product manager, Semiconductor Assembly Materials Dr. Andy Mackie is an expert on solder paste rheology (a word that we’ll explain in a minute). The following is a summary of a chat I had with him recently.

Dr. Ron: What is rheology?
Dr. Andy Mackie: Rheology is the study of the deformation and flow of matter. My fellow Lincolnshire (England) native Sir Isaac Newton postulated that if a fluid were flowing uniformly over a surface that it would exert a stress, t, (force per unit area) on that surface proportional to the gradient of the fluid’s velocity with respect to distance from that surface. The proportionality constant is called the viscosity, usually designated by m. The equation describing this relationship is:

It’s a lot more complicated if you figure in both non-laminar flow, and boundary layer effects as you get closer to the surface, but is a good guide to what is happening in the bulk of the fluid. For laminar flow, if the viscosity remains the same regardless of the shear rate, then this is a true “Newtonian fluid.” Glycerine is a good example.

Unfortunately, scientists soon found out that, for many fluids of engineering interest, viscosity is often not a constant, but varies with the fluid’s velocity gradient (often called the shear rate.) When the viscosity decreases with increasing shear rate the material is called a shear-thinning or “plastic” material. This needs to be differentiated from a “thixotropic” material, in which the viscosity decreases with time, although often the two are seen in tandem. There is also a phenomenon called “rheopexy,” where viscosity increases with time, then relaxes to its original viscosity — something you can experience if you grab a handful of wet sand at the beach, then watch it crumble back to its original relaxed state.

DR: Thixotropic or shear-thinning materials seem counter intuitive. Can you explain them in layman’s terms?
AM: Your kids see it every time they shake a bottle of ketchup. If you imagine that there is a very open structure built of little Lego bricks that holds the ketchup rigid in the bottle, then shaking temporarily smashes up the structure, making the ketchup much lower in viscosity and easier to pour. However, unlike Legos, the gel structure will eventually rebuild itself (a process called relaxation), the speed of which is primarily dictated by particle diffusion kinetics.

DR: What are some common thixotropic materials?
AM: Solder paste is a great example. Its shear-thinning nature is ideal for the printing process. The paste’s viscosity is low as it is experiences a high shear rate when forced through the stencil aperture, ensuring good hole fill and a high transfer efficiency. Then as the solder paste deposit rests on the leadframe, or wafer UBM or PWB pad, the viscosity is high, since the shear rate is near zero, enabling the deposit to maintain its shape and avoid “slump.” Any viscosity changes with time must then ideally either be small or have short relaxation time.

DR: Any final thoughts?
AM: The thixotropic and shear-thinning nature of solder pastes (and other similar electronics materials, for that matter) are very important, but are only part of the whole equation of solder paste stability and performance, as Indium scientists and process engineers are very well aware. I could get into another discussion about whether customers need “tack” or a means of keeping components in place, but I think I’ll leave that for another time. Cheers!

Let the Data Be Your Driver

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I was recently asked to give a presentation and audit an assembly line regarding minimizing “tombstoning” of passives at a major electronics assembler. As my presentation brought out, tombstoning can be caused by many factors: the reflow profile, the solder metal composition (for lead-free applications, SAC 387 tends to tombstone more than SAC 305), off-center placement, nitrogen reflow atmosphere, buried vias, etc. After two hours of talking, I walked the line that “had a problem with tombstoning.”

As I started asking, it became clear that no one knew the magnitude of the problem.

“How many passives are on each board?” I asked. No one knew.

“How many DPMO (defects per million opportunities) for tombstones have you had recently?” Also unknown.

As people scurried to get the data, it dawned on us that tombstoning might not be as big an issue as was thought. It was more of a local legend.

Finally, we got some data. Each board had about 1000 passives, and the company had produced 100 boards with a total of two tombstones in the past two hours. Tombstones were the only defect. Hmmmmm, two bad boards out of 100 = 98% first-pass yield, not bad! From a DPMO perspective, they had two defects per 200,000 (two defect opportunities per passive) opportunities or 10 DPMO, which is beyond world-class. This level of DPMO would be very difficult to improve on without massive engineering investment. It is “in the noise” and it is likely caused by “common cause” variation.

I then asked how much money it costs to repair a tombstone; as expected, no one knew. My guess was less than $2. This situation is the rare case where yields are so good, it may not pay to make engineering investment to improve them.

This isn’t the point of the story, however. In a case like this, the response — whatever it is — must be data driven. Only with the proper failure rate data, plotted in a Pareto chart, and a complete understanding of all costs, can the appropriate action plan be developed.

Always be data driven!

Tin Pest: A Forgotten Concern in Pb-Free Assembly?

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If tin pest were a living thing it might complain, “I can’t get no respect.” Reason: Tin whiskers get so much attention, while tin pest is forgotten.

Although my feeling is that tin whiskers are a greater concern, the number of recorded fails related to tin whiskers is less than 100. Compare this to the number of hard drive fails — about 100 million! With that in mind, let’s learn a little about tin pest.

Tin is a metal that is allotropic, meaning that it has different crystal structures under varying conditions of temperature and pressure. Tin has two allotropic forms. “Normal” or white beta tin has a stable tetragonal crystal structure with a density of 7.31g/cm3. Upon cooling below about 13.2ºC, beta tin turns extremely slowly into alpha tin. “Gray” or alpha tin has a cubic structure and a density of only 5.77g/cm3. Alpha tin is also a semiconductor, not a metal. The expansion of tin from white to gray causes most tin objects, afflicted with tin pest, to crumble.

The macro conversion of white to gray tin takes on the order of 18 months. The photo — likely the most famous modern photograph of tin pest — shows the phenomenon quite clearly.

This photo is titled “The Formation of Beta-Tin into Alpha-Tin in Sn-0.5Cu at T <10ºC" and is referenced from a paper by Y. Karlya, C. Gagg and W.J. Plumbridge, "Tin Pest in Lead-Free Solders," in Soldering and Surface Mount Technology, vol. 13 no. 1. 2000, 39-40.

The tin pest phenomenon has been known for centuries and there are many interesting, probably apocryphal, stories about tin pest. Perhaps the most famous is of the tin buttons on Napoleon’s soldiers’ coats disintegrating on their retreat from Moscow. Since tin pest looks like the tin has become diseased, many in the middle-ages attributed it to Satan as many tin organ pipes in Northern European churches fell victim to the effect.

Initially, tin pest was called “tin disease” or “tin plague.” I believe that the name “tin pest” came from the German translation for the word “plague” (i.e., in German plague is “pest”).

To most people with a little knowledge of materials, the conversion of beta to alpha tin at colder temperatures seems counterintuitive. Usually materials shrink at colder temperatures, not expand. Although it appears that the mechanism is not completely understood, it is likely due to gray alpha tin having lower entropy than white beta tin. With the removal of heat at the lower temperatures a lower entropy state would likely be more stable.

Because the conversion to gray tin requires expansion, the tin pest will usually nucleate at an edge, corner or surface. The nucleation can take scores of months, but once it starts, the conversion can be rapid, causing structural failure within months. The effect is also cumulative, so warming the sample will stop the growth, but it will continue once the sample is cold again.

Although tin pest can form at <13.2ºC, most researchers believe that the kinetics are very sluggish at this temperature. There seems to be general agreement in the literature that the maximum rate of tin pest formation occurs at -30º to -40ºC. What is the real risk of tin pest in Pb-free electronics? Not great. Modern researchers have had trouble reproducing it, even in the lab. The reason for this is likely that test samples contain small amounts of metal "contaminates" (<0.1%), such as bismuth, antimony, lead and a few other metals. These trace metals solid solution strengthen the solder and inhibit the expansion needed to form tin pest. Unfortunately, copper and silver (the typical Pb-free metals added to tin), do not appear tin inhibit tin pest growth.

Common Cause vs. Special Cause Defects

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In teaching process optimization and failure analysis, one of the most helpful concepts is understanding he difference between common cause and special cause defects. A special cause defect, in a well tuned process, occurs when something unpredictable changes. As an example, let’s say you get a batch of printed wiring boards (PWBs) that have oxidation on the pads. This is a defect and the boards shouldn’t  used, however we will assume that somehow they made it through the company’s receiving inspection process. It should not be too surprising that when the boards are assembled that they have a poor first-pass yield, say 35%. Typical first pass yield in this optimized process is 95%. It is obvious that the poor yield was due to this “special cause,” the oxidized pads.

Common cause failures are a little more difficult to explain and comprehend.  In a process, there are multiple entities that can vary, within the specifications, such as  the solder paste viscosity, the temperature and humidity of the room, the reflow profile, the wettability of the component leads and PWB pads, etc.  Statistically, within the specifications, the variation can be such as to result in a small number of fails … say the 5% we get with this process when everything is as it should be.  These types of fails are called common cause fails.

It is fundamentally crucial to understand the differences between special and common cause fails to successfully monitor and improve processes.  One of the tragedies that  I often see when the failure rate increases, due to special cause fails, is the process engineers changing the process parameters (e.g., raising the reflow temperatures when the pads in the special cause example above did not wet).  In a well optimized process the process parameters are determined by designed experiments, any collapse of process yields is the result of a special cause.  You can only fix special causes by identifying and rectifying them, not by changing the process!

Cheers,