Indium Corporation Blogs

Folks,

Many agree that the greatest technical achievement of human kind was landing a man on the moon and returning him safely to earth. (Just think about the fact that all of this was done with less computing power than your smartphone!).  Some of the folks involved in this effort  joined together to form The Right Climate Stuff research team.

For the past several years they studied the evidence for anthropogenic (human caused) global warming (AGW).  Less than a month ago they published their report.  I encourage everyone interested in this important topic to read their report. The team’s skepticism for AGW matches my own, point for point.   Their summary of their report follows:

• “Carbon-based AGW science is not settled. This refers only to the Carbon or CO2 role in induced warming
• Natural processes dominate climate change (although many are poorly understood).
• Non-carbon-based AGW anthropogenic forcings are significant. These include land use change, Urban Heat Island (UHI) effect, black carbon, and aerosols.
• Carbon-based AGW impact appears to be muted. Other sources are not necessarily muted; the impacts of changing solar activity, El Nino/La Nina-southern oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), black carbon, etc., are observable.
• Empirical evidence for carbon-based AGW does not support catastrophe.
• The threat of net harmful total global warming, if any, is not immediate and thus does not require swift corrective action.
• The US government is over-reacting to concerns about anthropogenic global warming.”

Cheers,

Dr. Ron

Image source: Right Climate Stuff Home Page

Folks,

A reader writes:

Dear Dr. Ron, I need to measure the void content of an alloy.  Is there an easy way to do it?

After a little thought, it occurred to me that the densities of the voided and unvoided material will likely hold the answer.  I derived the result below.  Assuming we know the density of the unvoided material, we can measure the density of the voided material with the Wet Gold Technique, discussed in recent posts, if the voids are not connected (closed cell.)  If the voids are connected (open cell), you could machine the foam to the shape of a rectangular parallelepiped and determine the density of the foam as the mass divided by the volume.

As an example, let’s say you have a closed cell aluminum foam. We use the wet gold technique to measure its density at 1.5g/cc. The density of solid Al is 2.7g/cc.

So the volume fraction of voids is:

Sadly, this technique could not be used to find void content in solder joints, or in BTC (e.g. QFN) thermal pad connections (which are so handily mitigated by using solder preforms.)

:   :   :   :   :   :   :

Global Warming Musings:  My recent post on GW generated many comments.   I will be sharing additional reasons why I am a skeptic at the end of posts like the one above. 

It is important to state the distinction between a GW Skeptic (me) and a GW Denier.  As a Skeptic, I am not convinced that the warming trends are alarming or unusual, especially since the atmosphere has not warmed in more than a decade.  Also, I am not convinced that the main driving force for the warming trend up to the late 1990s can conclusively be attributed to human activities.  Lastly, I’m not convinced that even with Draconian measures, we could affect a change that would matter.

The Carbon Cycle

In this post, I would like to share the data relating to how much carbon dioxide is produced and put into the atmosphere.  More specifically, what percent of carbon dioxide generated each year is from human activities. Would it be 30%, 40%, 50%?  The answer is 3%.  The remaining 97% of carbon dioxide generated on the earth each year is generated by natural processes in the oceans and on the land.  See the image below.  The GW argument is that even though human activities are only 3%, this amount offsets the delicate balance that nature provides.  Working with and modeling data all of the time, I find this argument unsatisfying.  Collecting accurate data and developing an accurate model on data like this is difficult.  Making incontrovertible conclusions (it is certain GW is caused by humans) more so. Freeman Dyson, arguably one of the most accomplished physicists of this era, has a similar view:

The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields and farms and forests. They do not begin to describe the real world we live in...

It is interesting also to note that throughout history the temperature of the earth determined the carbon dioxide content in the atmosphere, not vice versa.

Cheers,

Dr. Ron

.

Folks,

I am a global warming skeptic.  This term, however, requires some explanation.   I believe in climate change.  The climate is always changing.  However, I don’t think it is clear that the world has been getting warmer in the last decade.  I also am not convinced that humans are the main drivers of whatever  climate change has occurred.  The following explains why.

Point 1: For the Last 12 years the World Not Getting Warmer

The global warming scenario that exists today is that human emissions of carbon dioxide are the main reason that the climate is warming.  So, it is natural then to ask, is the climate really warming?  One look at USA Today’s cover story “Why You Should Sweat Climate Change” on 1 March 2013 would appear to settle the story.  Just look at  Figure 1 below.

Figure 1. Graph from USA Today Cover Story March 1,2013.

A quick scan of the graph shows 55.34°F last year and 50.56°F in 1895.  A 5 degree increase.  Wow!  A little closer analysis reveals that these temperatures are individual data points.  If you look at the years 1900 and 2010, you get 53°F for both years, essentially no change.  The thick red line is the long term trend and admittedly it increases from about 51.3°F to about 53°F in the 117 year period. 

Note the icon in the upper right corner, it shows the globe with a dramatic upward trend line.  However, this causes you to now note that this graph is for US temperatures, not world temperatures.  What was the world temperature like?  As this thought crosses your mind, you remember that 2012 brought Europe its coldest winter in recent memory. So you go on the internet and find out that 2012, for the world,  was the ninth hottest year on record.  Scary.  But then you think, “Wait a minute, that means that eight years were hotter.” So you wonder, what do the last 10 or so years look like for world temperatures.  The graph is below in Figure 2:

Figure 2. World Temperature 2001-2012. Graphed by Dr. Ron

Note that for the last twelve years, the trend is flat (actually a little down).  Where are all of the headlines sharing this important information?  So it is not clear that the world is continuing to get warmer.  

Am I the only one that finds it troubling that the media seem to universally tout the scary stories about global warming, but don’t seem to mention obvious counterpoints such as the graph above?  This information is profoundly important.

Point 2: In the Past, Nature Along has Delivered Stunning Climate Change by Itself

I am writing this post from my home Woodstock, VT. I look out my window and view two beautiful, large rocks, each about the size of a house.  These monoliths were likely left as the glaciers in the last ice age retreated, these rocks probably originated in Quebec.  Woodstock was under thousands of feet of ice during the last ice age, Canada was completely under ice.  New York State's Long Island is a glacial terminal moraine. The extent of the ice coverage is shown in Figure 3 below.  However, the forces of nature alone, raised the temperature of the earth by 12 degrees Celsius (with no help from mankind), melting the glaciers and allowing me to live in the Green Mountain State (Vermont = Ver (green) mont = mountain, in French.)  

Figure 3. The Extent of the Ice Coverage in the Last Ice Age  http://www.iceagenow.com/

The natural processes that caused the warming are many.  They include the precession of the earth on its axis, variation in the output of the sun, changes in the ocean and atmosphere, and others.  These processes  have resulted in the past temperature changes as shown in the Figure 4 below.

Figure 4. Temperature of the Earth in the Past 800,000 Years.

This figure shows as much as a 20°C (36°F!) temperature swing produced by nature alone.  The change in world temperature between 1900 and 2010 would be about as thick as the line in this figure. 

I find the proposition that the main driving force in global warming (if it is occurring)  being human produced CO₂ alone is hard to accept, when we see what mother nature has given us in the past.  It would be similar to someone taking the position that the only thing that affects stencil printing quality is the stencil.  When others point out that it might be the solder paste, or the print head, or separation speed, etc., they are shouted down as being unscientific.

Point 3: It was Warmer in the Middle Ages than Today

The United Nations commissioned a panel to study climate change in 1988.  The Intergovernmental Panel on Climate Change IPCC  was established.  In 1990, the panel came out with an assessment of past world temperatures as shown below in Figure 5.  The estimating of temperatures before the mid 1800s is difficult due to lack of records and thermometers before this time.

Figure 5. The First IPCC Assessment of World Temperatures, 800AD to the Present

There is some argument that the Medieval Warm period and Little Ice Age were local events, however they clearly existed and profoundly affected much of the Northern Hemisphere.  But more recent temperature IPCC plots lose them, as seen in Figure 6. below.   The Medieval Warm Period enable the Vikings to settle in Greenland and red wine to be grown in England. When the Little Ice Age came, the Vikings had to leave and England has not been as warm since. 

Figure 6. Third IPCC Temperature Assessment.  Note the Medieval Warm Period and Little Ice Age Disappear.  Because of the abrupt change in temperature after 1900, this graph has earned the moniker, The Hockey Stick Graph.

The controversy over the Hockey Stick graph  is interesting reading and is the source for Figures 4-6.  

In 2003, MacIntyre and McKitrick presented a detailed criticism of the IPCC 3^rd Assessment's Hockey Stick Graph in Figure 6 .  I find their criticism compelling.

I could go on and on,  but to summarize why I am a global warming skeptic:

• For the  past decade the world has not gotten warmer
• Natural forces overwhelm CO₂ as a driving force for climate change
• Sloppy science is behind the hockey stick graph

Please share your science- and fact-based comments.

Cheers,

Dr. Ron

Folks,

In my last post we saw how you could measure density with only a scale.  In this post, we will expand on that technique and learn how to measure metal content in gold/quartz ore.  In principle, this technique could be used for other ore, but the ores can only be two part (e.g. gold and quartz) systems.  Gold is a “natural” for this analysis as it is typically pure gold with quartz.

Gold is often found “veined” in quartz.  I was certain that this was the origin of the “Golden Fleece.”   The fleece being the white quartz with the gold on top.   However, a little research did not clarify this belief.

Anyway, let’s assume you take a few weeks off from work.  Leaving the world of solder paste, TIMS, ITO, wave solder flux and solder preforms behind, you set out for the west in search of some large gold nuggets.  Fate was with you in that, in a short time, you find a gold/quartz specimen as shown below.   The images, and the new “wet gold” weighing technique I will discuss, are from Bill and Linda Prospecting.

You are so excited you are shaking.  The only tools you brought are a scale, some string and a beaker.  To determine that gold content, you need to measure the weight of the gold in air and under water.  But you only have the scale as shown below.  What can you do?

After measuring the weight of the ore in air, fill the beaker part way with water, place it on the scale and zero the weight.  Then insert the ore on a string as shown below.  The scale will now read the weight of the volume of water that the ore displaces.  Let’s call this weight of the water displaced W_D .  The wet weight of gold (weight of gold under water) will be the weight in air minus W_D.  So we now have the weight in air and the weight in water.

The derivation of the equation that tells us how much gold is in the ore is at the end of this post.  The final equation we need is W_Au = 3.07W_W – 1.91W_Air.  For our ore sample W_Air = 25.1 pennyweight (pw). A pennyweight is 1/20^th of a troy oz.  W_D as shown in the photo above is 8.3 pw.  So W_W = W_Air – W_D = 25.1-8.3 = 16.8 pw.  So W_Au = 3.05*16.8 – 1.91*25.1 = 3.635 pw.  Subsequent analysis showed that the gold content was actually 3.9 pw and error less than 7%.  Not too bad for a simple field measurement.  At $1600/oz our ore sample contained. a little over $300 dollars of gold.

This technique could be used to measure the density of an alloy as in the last post.

Cheers,

Dr.Ron

The Derivation of the Equation

Folks,

In the category of interesting requests, Ron, a gold worker, from Guyana, sent me the following note:

Dr. Ron,

My colleagues use a “wet” gold technique to measure gold alloy density.  Is this valid?  Where does the formula come from?

Sincerely,

Ron

Well, to tell the truth, I had never heard of it and was skeptical.  How can you measure density (mass/volume) by only measuring weight?  So, I investigated. The technique claims that one can measure density with only a scale, by measuring the alloy’s weight in air and in water.

I could find no derivation, so I thought about it and derived it on my own.  As far as measurements go, as stated, you only have to measure the weight in air and water.  If you don’t have a scale that can handle being immersed in water, you can use a hanging scale (think weighing a fish).  So, after weighing the alloy in air, you immerse it in water. It will weigh the amount of water it displaces less.  The derivation is below:

As an example, let’s say you have a gold alloy ingot that weighs 1,000 grams (OK, I know grams is mass, but we are all sloppy and use it as weight, too) in air.  You weigh it in water and it weighs 930 grams. From the formula below, the alloys density is:

r = 1000/(1000-930) = 14.29g/cc

Since the density of gold is 19.3g/cc, the alloy is not pure gold.  If you knew the alloying element, say copper, you could use Indium’s Solder Alloy Density Calculator to determine that the alloy was 69.8% gold, 30.2% copper.  If there are multiple alloying elements, since most of the common elements have a density of about 9 g/cc, you can even estimate the fineness of the gold.

Could this technique be used to measure the alloy density of say a handful of solder preforms. Sure, you could put them in a woven bag of non-hygroscopic material and weigh them in air and water.  Admittedly, measuring the density of solder paste, with this technique, would be a challenge.

Next posting, I will show how this technique is used to measure the quantity of gold in gold/quartz ore.

Cheers,

Dr. Ron

Folks,

I was at APEX 2013 San Diego this past week.  San Diego is a great venue for the show, but I always forget how cold it can be (55-65°F) this time of year.  The folks at iConnect 007 interviewed me at the show; the topic was lead-free reliability and has cost for consumer electronics been demonstrated.  You can see the interview here.

These are topics I think about often, so let’s discuss them a bit. First, let’s consider reliability.  RoHS was enacted on 01 July 2006, more than 6 ½ years ago.  Each year more than $1 trillion-worth of electronics are made, therefore, in this period of time, something over $3 trillion worth of consumer electronics have been manufactured.  There have been no “the sky is falling”-type of reliability issues in this time.  How can I say this?  Well, my office at the Thayer School of Engineering at Dartmouth is across the hall from the IT (information Technology) Dept.  They purchase all of the millions of dollars worth of PCs, printers, displays etc. that Thayer uses.  Several years ago (say early 2011) I stopped by when most of the department was in and cheerfully asked if the reliability of the equipment they purchase has gone down since lead-free assembly was enacted.  They asked me in unison, “What’s lead-free assembly.”  After I explained what lead-free assembly was, they confirmed that they have noticed no changes in reliability.  Since RoHS, my family has purchase about 100+ electronic devices, a few have had reliability problems, about as many as in the past.  Most were attributed to hard drive fails.  Of the scores of friends and colleagues I have, no one has ever commented that they have noticed an increase in electronics fails. So, my conclusion is that consumer product reliability is not "practically" worse if my family and  these many  other folks haven’t noticed it.

I have made an informal study of reliability data of lead-free vis-a-vis tin-lead solders published in papers.  A statement from Rockwell Collin’s JCAA/JGF-PP No Lead solder Project: -55C-125C Thermal Cycle Testing Final Report  sums up my overview conclusion nicely: “Test vehicles assembled with lead-free materials (notably tin-silver-copper) exhibited lower reliability under some test conditions.”  Nay sayers might be quick to suggest that this statement  says that lead-free is no good.  However, the statement could be reworded to say: “In considerably more than half of the test conditions, test vehicles assembled with lead-free materials had higher reliability." Counting the comparisons in the Rockwell Collins paper shows lead-free better in 51 cases, tin-lead better in 31 cases, and one draw.  However, it is disturbing that a small percentage of lead-free assembled test vehicles had much much worse reliability than tin-lead test vehicles.  This later information makes me believe that lead-free is not yet ready for mission-critical, high-reliability, long-life products.  These small numbers of much poorer reliability assemblies must be understood and corrected before lead-free is ready for mission-critical prime time.  The much shorter life cycle of today’s consumer electronics may also mask this concern.

What about cost?  I don’t at all want to minimize the expense that many went through to go lead-free and RoHS compliant.  In about 2007, one of our colleagues estimated that it cost the electronics industry $20 billion to become RoHS compliant.  I think this number is low, but, from a consumer’s perspective, there has been no cost hardship.  The price of a PC continued to go down during and after RoHS implementation, as shown in the figure below.  While performing my non-scientific survey of co-workers, family, and friends on reliability, I also asked about cost.  All agreed, electronics are cheaper than ever.

Challenges still exist, even in consumer electronics with the Head-in-Pillow, Graping, non wet opens, and other defects.  However, we can all purchase lead-free, RoHS compliant products at a reasonable cost and reliability.

Cheers,

Dr. Ron

The source for the image is :http://thomaslah.wordpress.com/2010/02/03/apple-and-intel-defying-gravity/

Folks,

Our discussion of Weibull Analysis continues.....Let’s say you have worked hard and assembled some SMT lead-free PCBs for thermal cycle testing.  You used the best lead-free solder paste, and some lead-free solder preforms as you assembled several through-hole components with the Pin-in Paste process.  You were a little concerned with the assembly process as the board was thermally and physically massive and the reflow process needed to be a bit above the recommended temperature and time.

The results of the thermal cycle testing are shown in Figure 1 below.  You dutifully report the characteristic life (or scale) as 2,387 cycles and the first fail at 300 cycles.  You were quite disappointed, as in the past similar, but slightly smaller boards, had a slightly higher scale, but more importantly, the first fail was about 1,000 cycles.  Anyway, you write you report up and file it away.

Figure 1. A Weibull Plot of the Thermal Cycle Data

Hold on!  The data are screaming at you the something is going on.  Look at the same data in Figure 2.  Note two distinct lines shown in green.  These two separate lines suggest very strongly that there are multiple failure modes.  The line furthest to the right is likely the typical failure mode observed in the past.  The line to the left is a new early failure mode.  It could be due to something like oxidized pads or some other phenomena not seen when testing similar but smaller boards.  Root cause failure analysis should be performed to try and understand to new failure mode.

Figure 2. A Weibull Plot of the Thermal Cycle Data with Multiple Failure Modes Noted

Now for a human interest note:

One of the rewarding aspects of being a professor at Dartmouth is the outstanding nature of many of the students.  They are not just good academically, but often are talented artistically, athletically, etc.  This point was brought home to me recently.  In a class I teach, ENGS 1: The Technology of Everyday Things, we were recently discussing the conservation of angular momentum (CoAM).  One of the most striking ways to demonstrate CoAM is an ice skater’s spin.  I went on the internet and could not find a good video of a spin.  I then remembered that one of my former students, Julia Zaskorski was on Dartmouth’s figure skating team.  I asked her if she had a video she could share.  It appears below.  She is a materials science and physics major.  Who knows, maybe we will see her at APEX or SMTAI in a few years. 

Here is a little bio in her own words:

               My name is Julia Zaskorski, and I’m a junior from Wellesley College taking part in the 12 College Exchange Program at Dartmouth.  At Wellesley I am majoring in physics with the intent to pursue mechanical engineering.  Despite Wellesley’s relationship with nearby MIT, Wellesley does not have its own engineering program, so I sought out the more self-contained curriculum and atmosphere at the Thayer School of Engineering.  In addition to the draw of the Thayer School, the Dartmouth Figure Skating team was also a hugely motivating factor for my exchange, as Wellesley does not have a team, let alone a rink.  I have known the coach of the Dartmouth team for several years now, and to finally see my name on the roster for the team is a dream come true.  The engineers, as well as the winter activities here in Hanover, pulled my heart to Dartmouth long before I’d ever set foot on campus. 

  Cheers,

  Dr .Ron                           

Folks,

In continuing our discussion on Weibull Analysis, let’s assume we assembled some SMT and through-hole PCBs with lead-free solder paste.  On this board are also some bottom-side terminated (BTC) components (often called QFNs), that are also assembled with solder preforms.  A stress test is performed to test the BTCs.  In such a test, the first fail in Weibull analysis is the most important data point.  No matter the results of remainder of the data, these later fails cannot undo the effect of a very early first fail. 

To understand this concept, let’s look at the Weibull chart below.  In many high reliability applications, there may be a requirement that some small percentage of the components under test have at least some minimum reliability.

Figure 1.  Weibull Analysis with an Early Fail.

As an example, let’s say that 1% of the components cannot have less than 500 cycles of life.  By looking at Figure 1, we see that 1% have less than 150 cycles of life (see arrow.)  This one early outlier dramatically affects the Weibull Analysis.

However, if that outlier was removed, as seen in Figure 2, the data suggest that 1% of the components will have a life of 900 cycles.  We can see the dramatic effect the first fail has on this result.  Note that the first fail does not affect the “scale” or characteristic life much (2647 vs 2682).  Hence, the characteristic life, is not a robust metric to use in a high reliability environment.  However, the shape or slope is dramatically affected by the early fail as it changes from 2.22 to 4.23 when the early fail is “censored.” 

Figure 2. Weibull Analysis with the Early Fail Removed (Censored).

Why might an outlier like this exist?  Almost certainly there is something unusual about the early fail.  It might be something like an oxidized pad preventing good wetting of the solder.  Perhaps something like this failure mode might be discovered in root cause failure analysis.  However, I am typically opposed to censoring data, even with supportive failure analysis.  I think the test should be done over.  It is often too easy to talk yourself into accepting inconclusive failure analysis.

What is your opinion?

Cheers,

Dr. Ron

Folks,

A while ago I discussed the Weibull Distribution and its importance in electronics reliability analysis.  This distribution has been used to evaluate the life of solder joints whether formed in SMT, wave, or even using solder preforms. In the next few posts, I would like to discuss how to interpret Weibull plots.

Let’s consider two Weibull plots from thermal cycle testing of lead-free solder joints as seen below in Figure 1.

Figure 1.  A Weibull Plot of Thermal Cycle Data for Alloy 2 and Alloy 4.

Both alloys have almost exactly the same scale, or characteristic life. You will remember that characteristic life is the number of cycles at which 63% of the test subjects fail.  For Alloy 2 it is 2,593 cycles and for Alloy 4 it is slightly better at 2,629 cycles.  However, these two alloys performed dramatically differently.  The most striking difference is in their “spread.”  We see this much greater spread for Alloy 4, when we plot a fit to the data as a normal distribution, as in Figure 2 below.

Figure 2. The Best Fit Normal Distribution Plot for Alloy 2 and Alloy 4.

In the Weibull plot, the data for Alloy 2 has a very steep slope or shape factor, this indicates a tight distribution.  A tight distribution is desirable as it facilitates more accurate prediction of thermal cycle life.  Alloy 2 is clearly superior.  So, in a Weibull distribution, not only is a large scale factor or characteristic life desired, but so is a steep slope or larger shape factor.

Next time we will talk about outliers.

Cheers,

Dr. Ron

Folks,

Let’s step away from electronics assembly challenges, and deep considerations of solder paste, solder preforms, and wave soldering, to ponder where electronics have gone in the last decade or so. 

The mobile phone of the early 2000s was just that, a phone. Today it is a phone, music player, PalmPilot-type organizer, camera (still and video), video player, gamer, TV remote, GPS system, web portal, etc.  There is almost nothing electronic that it can’t do.  The USB memory stick of 2002 with 0.5GB of memory cost $500, today $5 will get you 4GB one, a cost reduction of 800 to one, the equivalent of halving in price about every year.

There is no reason to expect any less dramatic advancements in the future.  But, predicting the future of electronics is never easy.  In the January 2013 edition of Scientific American  Ed Regis wrote an article titled, The Bold and Foolish Effort to Predict the Future of Computing. In this article, Regis interviewed eight computer luminaries, including Stephen Wolfram and Nathan Myhrvold,  to ascertain their perspectives on where computing will be in 150 years.  The conclusion was that no one can predict the future of computing, as interviewee George Dyson said, “All I can guarantee is that any prediction will be wrong."

One person less humbled by the difficulties of computing predictions is Ray Kurzweil.  His prediction success level of more than 80% would seem to support such confidence.  Kurzweil also just got a new job at Google. I am finishing his new book “How to Create a Mind: The Secret of Human Thought Revealed” and, while I  finding it fascinating, I think he goes too far.  He believes the mind is a sophisticated computer and that, when computers get to a certain point equaling and surpassing the human mind's computational ability, they will be considered human.

Supporting this point, he hopes to, someday, resurrect his father, as Bloomberg states:

“Among the stranger things Ray Kurzweil will say to your face is that he intends to bring his father back to life. The famed inventor has a storage locker full of memorabilia—family photographs, letters, even utility bills—tied to his father, Fredric, who died in 1970. Someday, Kurzweil hopes to feed this data trove into a computer that will reconstruct a virtual rendering of dear old Dad.”

Call me a religious fanatic, but I think there is something more to each of us than our memories and our brains computing ability. 

Kurzweil endorses IBM's computer system, Watson’s victory in Jeopardy  in February of 2011 as a major step in the direction of computers as humans.  IBM provided commercial support for these Jeopardy episodes.  In the commercials they strongly reminded us that Watson was not thinking, but only doing what it (not “he”) was programmed to do.  Someone summed it up nicely, Watson won, but did he know he won?

I think there are a few major things that people like Kurzweil minimize when they propose that computers will be recognized as human.  These points are:

1. Humans are sentient (they would know whether or not they won or lost Jeopardy, we have emotions and feelings).  I know of no progress in sentience development for machines.
2. Humans have a will.  We get up in the morning, we decide what we will do that day and do it.  There is no progress (thankfully?) in giving computers a will.
3. Humans have a biological body.  We smell the newly cut grass, feel a refreshing breeze, get tired, enjoy a meal, enjoy sports etc.   It is easy for some to minimize the importance of the body in being human. Again no progress in this area.

However, I don’t want to minimize much of what Kurzweil predicts.  In her ground breaking book, Alone Together, Sherry Turkle tells us that, in addition to the fact that the average teenager in the US sends 200 text messages a day, electronic companions already exist.  As time goes by they will become more realistic and will be capable of interesting and stimulating speech and interaction.  Having all of the world’s knowledge at their fingertips (pun intended), these companions will likely be more stimulating than people, they will easily pass the Turing Test, and, for good or ill, will make us more “alone together” than ever.  But our companion will not love, fear, hate, or know that it is a companion.

As has been pointed out, this brave new world is coming whether we like it or not.

BTW, on another topic,  the History Channel has produced a terrific video series, Men Who Built America http://www.history.com/shows/men-who-built-america.  It is a the spell-binding story of Vanderbilt, Rockefeller, Carnegie, J. P. Morgan, Edison, and Henry Ford.  If you missed it, it is coming out in DVD in January.

Cheers and Best for the New Year,

Dr. Ron

Folks,

Let’s see how Pete is handling the wave solder crisis.

Pete had to admit that he was surprised by the positive outcome of his meeting with Fred Castle.  He had sent Patty a text the day before, after he took the operators to lunch, before meeting Fred.  The text was a little negative.  So he was eager to send her the good news about the surprises in his two meetings with Fred since then.  He was frustrated that he kept on getting her voice mail.  Finally she answered.

“Advanced Processes,” Patty speaking.

“Hey, kiddo, it’s your favorite process genius!” Pete responded cheerfully.

“Oh, this must be Oscar Patterson!” Patty joked, and they both laughed. Patterson was an annoying chap they had to deal with a few years ago.  He topped their list of most annoying people. Pete had almost come to fisticuffs with him.

“How is it going there?” Patty asked.

“Shockingly well. My meetings with Fred Castle were very productive” Pete answered.

“Well, that is shockingly positive news. But I thought he said, ‘I’ve forgotten more about wave soldering than you’ll ever know,’” Patty responded.

“That’s the first thing he said to me when we shook hands, but he was clearly teasing.  He slapped me on the back at the same time and chuckled. He went on to say that he had worked in wave soldering for over 30 years, typically at companies that had processes that were out of control.  It was clear that he understood a lot about wave.  We talked for 30 minutes about what makes a good wave process. As far as I could tell he was right on in everything he said.  I think the operators didn’t pick up on his teasing, by the way,” Pete elaborated.

“What about special cause vs common cause?” Patty queried.

“He didn’t have a clue,” Pete replied.

Patty was bracing herself.  She was concerned that Pete might have insulted Castle.

“And you didn’t tell him he was an idiot?’ Patty teased.

“Patricia! I’m shocked you could even think such a thought,” Pete replied.

Pete went on, “We bonded, and he admitted that he was frustrated with the yield loss increasing.  He was studying the situation and spending a lot of time trying to figure out the issues.  He said he was having trouble sleeping.  He mentioned that, in his last job, he was responsible for the wave processes at 10 locations.  He was constantly fighting fires and got good at it.  He had never worked at company that performed DOEs and developed optimized processes.”

“I’m dying to know how this situation worked out,” she interrupted.

“Patience, patience,” Pete admonished jokingly. He continued, ”It was clear that Fred likes to learn, so  I mentioned that, recently, The Professor had mentioned the importance of understanding the differences between common cause and special cause variation when trouble shooting a process.  I suggested that maybe studying these topics might help. So I gave him a few links to The Professor’s posts on common cause and special cause.” (Dr. Ron note, it will be helpful understanding this story to read The Professor's post, if you are not familiar with common cause and special cause fails.)

“What happened then?” Patty asked, the impatience in her voice apparent.

“Remember, this is now the end of my first day. I watched the process in the morning, took Molly and Chuck to lunch, and then met with Fred.  On the second day I had a morning meeting with the quality director, Pam. Then Castle and I went to lunch,” Pete elaborated.

“And?” Patty asked impatiently.

“Castle was all excited.  After studying common cause and special cause all night, he realized that he was seeing common cause fails in his detailed scrutiny of the wave line. By adjusting the process parameters slightly when he found a common cause fail, he was moving away from the optimized process settings that were determined by a DOE, so the failure rate got worse.  In his previous job, he was mostly seeing special cause fails, as the processes were not optimized, so he was used to intervening,” Pete explained.

“It seems like he won’t have enough to do now,” Patty commented.

“I suggested he help quality.  They are stretched thin and he is a detailed-oriented fellow.  He keeps meticulous Pareto charts of the fails,” Pete said.

So, where are things now?’ Patty asked.

“Yesterday and today, first pass yields are at 96%.  Fred also started helping quality today. It felt good to help and not offend,” Pete finished.

Patty thanked Pete for the great job he did and complimented him strongly for being successful and making friends at the same time.  As she hung up the phone, she saw an email from Pam Olinski in her in box.  It was a kind note thanking her and Pete for his help.  It recounted much of what Pete had said.

She wistfully looked out her window.  She was happy and grateful for all of her success, but, to be truthful, she missed the action of being out on the shop floor solving these types for problems.

She was jolted from her chair when she suddenly remembered it was her turn to take her twin sons to karate lessons.  So she packed up quickly to pick them up at her mother-in-law's, to get them to the gym by 5PM.

Cheers,

Dr. Ron

image

Folks,

It's been a while. Let's look in on Patty......

Patty stared, bleary eyed, at her laptop screen.  It was the day after the election.  She and Rob were following the election closely as a “statistical thinking” exercise.  They had met at a conference with The Professor in late October and agreed that following the election would test their statistical thinking skills.  They established beforehand that they would not discuss who they favored, just the data.

All agreed that Mitt Romney had a greater challenge than President Obama.

As Rob said, “Of the six most populated states, even the Republicans agree that Obama will win California (1), New York (3), Illinois (5), and Pennsylvania (6).  Romney is only a shoe-in for Texas (2).  Only Florida (4) is a toss up”

“I thought some analysts were saying that Pennsylvania is in play,” The Professor commented.

“They’re dreaming,” Patty said with conviction.  “Pennsylvania has too many big cities; typical democratic strong holds,” she continued.

“Many pollsters have 255 electoral votes in Obama’s column and only a little over 200 for Romney. It’s hard to see a Romney path to victory.  It is statistically unlikely he could win all of the swing states” Rob added.

The Professor beamed as he listened to his protégés' intelligently analyze and argue the situation.

They all agreed that it was hard to understand why many were referring to it as a close race, although voter turnout could change everything.

As election night went on, Patty felt she could call the election at 8PM EST.  However, she was sympathetic that the networks needed a high level of certainty. The major networks were finally calling it at 10PM.  When they did, Romney was ahead in the popular vote by about 1 million.  Patty chuckled to herself, when a renowned TV anchor commented that it might be a governing challenge to Obama to win the electoral college and not the popular vote.  Clearly he had not factored in the fact that, although California was “called” for Obama around 10PM EST, it was called with only a few percent of the votes in.  The networks were using exit polls and statistical analysis to make a projection.  By the time all of the west coast votes were counted, Obama will comfortably win the popular vote - because of California’s large population.  Patty thought this should be obvious to the pundits.

Patty had stayed up until about 11PM to watch the results.  It was comforting that her analysis was spot on.  However, she was so “wound up” that she couldn’t fall asleep and she was now paying the price.

As her attention shifted back to the email she was writing.

Suddenly, she was jarred by a loud, cheerful voice.

“Hey kiddo, pack your bags, looks like we’re on the road again,” Pete said loudly.

As usual Patty thought,” How does Pete always know these things before I do…..I’m the boss!”

“What’s the scoop?” Patty asked.

“Remember our facility in Ohio?  They are having wave soldering yield and throughput problems,” Pete answered.

“What!” Patty shouted.  “We spent a lot of time there six months ago optimizing their wave soldering operation and teaching them the appropriate use of solder preforms. What happened?” She finished.

“Not sure,” Pete responded. “I thought we worked really well with their team and developed a good process.  It seemed to me it was one of the more productive projects I was involved in in quite awhile,” Pete finished.

“And you didn’t even offend any of the senior managers,” Patty teased.

Pete chuckled but his cheeks did turn a little red.  Pete was a terrific process engineer, but he had a little bit of a “short fuse,” although he was usually right.

“In talking to some of my buddies there, they told me that senior management hired a very senior fellow who is considered an expert in wave.  Strangely, things fell apart right after he joined,” Pete explained.

“Well, you are on your own for this one.  I've got a number of family commitments over the next two weeks,” Patty said with a little sadness in her voice.  Patty enjoyed these types of challenges.

“As soon as I get the official request, you’ll be on your way,” Patty said.

“Oh, and don’t offend anyone,” she teasingly finished.

As Pete left her office, she checked her emails. Sure enough, there was a note from Mike Madigan asking her to intervene in this wave soldering problem.

Two days later Pete was in ACME’s Ohio facility sitting in the office of Pam Olinski, the site's quality manager.

“Pete, I’m so glad you could come.  Three months ago our wave soldering first pass yield was 95% and our production was about 2,000 boards per day.  Yield is now 90% and production is off 15%. “Help!” Pam said.

“Tell me about the new guy?” Pete asked.

“Fred Castle; he has very impressive credentials, but he has been running the wave process like a dictator. He stops the process a lot to adjust the wave machine.  I think he will be offended that you are here to audit the process,” Pam finished.

Because of this concern, they agreed that it might be best to have Pete initially view the process from afar.  So, they decided that Pete would be given an operator’s smock and walk around the shop floor for half a day or so.

As Pete arrived on the shop floor, almost immediately he saw Fred stop the wave machine and make some adjustments.  While  making the adjustments, Fred held a board in his hand - and he looked at occasionally.  After the wave machine was running again, Pete saw that Fred looked carefully at every board.

Pete saw one of the wave operators was going on a break.  Pete remembered Molly Stark from his visit to optimize the wave process six months ago, so he stopped her and ask if she could join in for lunch.

The morning passed quickly, and Pete was off to lunch with Molly.  As Pete had suggested, Molly brought another operator, Chuck Petrus to lunch.  Pete insisted on treating, so Molly and Chuck left their brown bags behind. 

In total, Fred stopped the line four times during the almost 4 hours of Pete's observations. Each time he made adjustments on the wave machine. After exchanging pleasantries Pete asked, “Why was that fellow stopping the wave line so often?”

Molly got quite animated and answered, “That’s Fred Castle, the supposed wave genius. He stops the line every time there is a defect and adjusts the wave machine parameters.  A number of us complained to him that he shouldn’t make adjustments on the machine that with just one fail.  That’s what you taught us.”

“What did he say?” Pete asked.

“ ‘I’ve forgotten more about wave soldering than you will ever know’……No one has said a word since,” Chuck responded.

“You and Patty taught us about special cause and common cause variation. I don’t think Fred understands that,” Molly commented.

“He’s also a knob twiddler,” Chuck added.

Does Fred know the difference between common and special cause variation?  Is that the root of the yield and throughput problems? 

What is a knob twiddler? Stay tuned to find out.

Cheers,

Dr. Ron

Folks,

In the last posting, we saw how Weibull analysis helped us to determine that SACM lead-free solder (SAC105 with about 0.1% manganese) has comparable (actually better) thermal cycle performance versus SAC305 solder.  Software like Minitab will give us even more detailed information about the performance of the solder joints in stress testing as we see in Figure 1, above.

In addition to the Weibull plot, we also have the Probability Density Function (PDF), the Survival Function and the Hazard Function.  The PDF tells us when it is most likely that a test board will fail in a test population, as shown by the inserted red line.  We see that it is a little less than 2,000 cycles.  The Survival Function shows the percent of surviving test boards.  We observe that the expected life (the 50% point) is quite close to the maximum of the PDF.  The Hazard Function tells us the rate at which the test boards are dropping out.  It increases with time, but there are few boars left so the PDF drops down at the end of the test, even though the fall out rate is the highest.

It is interesting (and perhaps appropriate as Halloween approaches) to consider if human mortality follows a Weibull distribution.  I used some data for the Centers for Disease Control  that are a little over ten years old, for males in the US.  So, the mean life expectancy is a little low at 72 years.  (I was a little lazy, the old data were a little easier to work with than new data, some conversions are needed to make it work.) The data appear above in Figure 2. 

As you can see, just like a solder joint, your life expectancy can be modeled quite well by the Weibull distribution.

Cheers,

Dr. Ron

Derivation of the Weibull Graph

Folks,

Last time we introduced Weibull analysis. Let's now derive the relationships needed to calculate the slope, beta, and characteristic life, eta.

F(t) is the cumulative fraction of fails, from 0 to 1. By choosing Ln(t) as x and LnLn 1/(1-F(t) as y, we would expect a straight line.  See the derivation above.  It can be shown graphically that this fact is so.  So if we plot F(t) versus t on logarithmic graph paper, the slope of the line will be beta. To determine eta, let t=eta, in the first equation below.  The result is F(t) = 1-e^-1 = 0.632.  So the time at which 63.2% of the parts have failed, is eta, the characteristic life.

Let’s consider some data comparing SAC305 and SACM (SAC105 with about 0.1% manganese) BGA solder balls in thermal cycle testing.   The primary test vehicle employed was a TFBGA with NiAu finish mounted on PCB with OSP finish.  SACM is a new breakthrough soldering alloy that has better drop shock resistance than SAC105 and comparable thermal cycle performance to SAC305.  The data follow.  The first column is the sample number, the third and fifth columns are the number to thermal cycles to fail for SAC305 and SACM.  The second and forth columns are rank of the sample number.  One would think that the first number in the second  column would be 100*(1/15) =6.67%, as it represents the cumulative percent of samples failed, but a slight correct factor is needed.   By plotting the log log of rank as shown above (LnLn1/(1-F(t)) vs log of cycles at failure, we get the Weibull plot.  The slopes of the best fit line is equal to beta and the number of cycles at rank = 63.2% is eta.

Fortunately software like Minitab 16 does the plotting and calculating of beta and eta automatically.  The results are below:

We see that the shape (beta) for SAC305 is 1.76 and that of SACM is 6.09, the scale or characteristic life (eta) is 1736.8 and 2016.8 respectively.  These results are a strong vote of confidence for SACM.  Its steep slope (high beta) suggests a tighter distribution, with more consistent solder joints and its characteristic life (eta) is also slightly greater.

I plan on teaching detailed workshops on this topic.  I will keep you posted.

Cheers,

Dr. Ron

Folks,

The Weibull distribution is arguably the most important distribution in failure analysis of leaded and lead-free solder joints.  It is the first thought of someone trying to model thermal cycle, drop shock, or other failure modes associated with through-hole and SMT assembly.

The Likelihood of Getting Heads in 60 Coin Tosses is Described by The Binomial Distribution

The Weibull distribution was invented by Waloddi Weibull in 1931.  This invention fact was recounted by Dr. Robert Abernethy in his famous textbook on Weibull analysis, The New Weibull Handbook. This statement may not seem unusual, until we ponder that all common distributions in statistics were discovered, not invented.  The three most common statistical distributions are the Normal, Poisson, and Binomial distributions. As an example of a discovered statistical distribution, let’s consider the Binomial distribution.  This distribution describes, among other things, the odds in flipping a coin.  If you flip a fair coin 60 times, you are most likely to obtain 30 heads (H) and 30 tails (T), but getting 29 H and 31 T or 32 H and 28 T would not be all that uncommon.  Mathematical analysis shows that the curve below results.  If a coin flipping experiment is performed many times, this curve will faithfully predict the results.  The curve is not invented it is discovered from the deep theoretical underpinnings of the Binomial Distribution.

Waloddi Weibull 1887-1979

The fact that the Weibull distribution was invented suggests that Weibull selected it because it fit many types of failure data.  He defined cumulative Weibull distribution is defined as:

Where eta is the characteristic life or the scale function and beta is the slope, were as F(t) is the cumulative fraction of failures.  Weibull proposed this function because for beta less than 1, F(t) describes “infant” mortality fails.  In this situation the failure rate is decreasing with time. For beta greater than 1, it describes “wear out” failures, where the failure rate is increasing with time.  In electronics, we typically try to weed out infant mortality by using “burn in.” For beta equal to 1, the failure rate is constant.  These three scenarios are shown in the figure below.

So typically, in electronics failure analysis, we are plotting failure data versus time to determine beta and eta, typically with software like Minitab®.

In the next posting we will analyze some failure data to determine eta and beta and discuss their significance.

Weibull himself was a curious character and much of the available information on him is chronicled by Abernethy. 

For sure Weibull was a vigorous man.  His second wife was almost 50 years his junior and he fathered a daughter at about 80 years of age!

Cheers,

Dr. Ron

Folks,

Everyday, we are exposed to the results of surveys and polls.  A typical example might be that President Obama is leading Mitt Romney in a poll by 48% to 45%, but the results are not statistically significant.  A reasonable question might be, “What does it mean to be statistically significant ?”  

To determine statistical significance, typically, the statistician will use the criteria that if there is only a 5 percent or less chance that the conclusion would be wrong, it is considered statistically significant.  So, when another poll would state that President Obama leads by 49% to 44% and it is statistically significant, there is, statistically, less than a 5 % chance that the conclusion is wrong.  The 5 % criteria is not cast in concrete. Sometimes 10%, 1%, or even 0.1% might be used.  However, tradition has given us 5% as the default value for “statistically significance.”  It is also helpful to understand that, the more data points in the sample, the more likely the results will be statistically significant.

But if some data are statistically significant, is it always "practically" significant?  As an example, let’s say that you really like chocolate.  Your favorite brand is in a taste test and it scores 9.6 out of 10, whereas a new chocolate scores 9.7/10 and the results are statistically significant.  On the downside, the new chocolate costs 5 times as much.  Is it worth the extra money to convert to the new chocolate? In this case, we have to ask, is the difference practically significant.  The answer is, in all likelihood, no.  Such a difference as 0.1 point out of 10 is very small, and taste is also subjective.  Here, the result might not be practically significant.  The subjectiveness of a taste test may mean that you either can’t tell the difference or that you still like your favorite chocolate the best.

Let’s consider another less subjective example.  Suppose that, in a certain application, solder voiding  is a critical concern.  So, you measure the voiding of two solder pastes.  After collecting hundreds of data points, you find that the average voiding of one solder paste is 8% and that of the other is 7%.  Analysis with Mintab® software tells you that the difference is statistically significant.  But is the difference practically significant?  Probably not. 

How do you determine practical significance? Typically it would be by experimentation or in some cases by experience.  In our example of solder voiding, suppose experiments showed that, as long as the voiding average is below 30%, there will be no concerns.  In light of this, engineering may have set a specification that voiding must not be greater than 25% on average.  (All of this discussion assumes that the spread or standard deviation of the data is not large, but this subject is the topic of another discussion.)  So, in this case, the difference between 7 and 8 percent voiding may be statistically significant, but not practically significant.  So, a prudent engineer may select the 8% paste if it had other desirable features, such as better response to pause, or resistance to graping, or improved head-in-pillow defect.

So always ask yourself, is the difference both statistical and practical.

The image shows solder joint graping, which is often more of a concern than voiding.

Cheers,

Dr. Ron

Folks,

Let's see how Patty and Pete are making out on their latest adventure....

“Here is the ProfitPro™ output," Dave Ferris said as he pointed to a PowerPoint slide on the screen.

Just then, the site general manager, Sally Wilson, and the head of purchasing, Blaine Ellis, arrived. 

“Long time no see,” Pete said to Ellis.

Ellis acknowledged Pete, but appeared to be in a foul mood.  Everyone settled down and the meeting came to order. Patty was again surprised, Pete always seemed to know everybody.

After introductions, Sally kicked off the meeting.

“As you know, we have a new corporate award program for saving money.  Dave is a candidate to win the first award.  But Blaine won’t sign off on it, because his solder paste expenses have, in his word, ‘skyrocketed' ”, Sally started.

Ellis exclaimed, “My solder paste costs are through the roof.  Last year we used 3,000 kilograms and this year we are using 3,100 kilograms and each kilogram costs $10.00 more.  That’s more than $40,000 dollars more.  How is this saving money?”

“How has the overall site profitability changed?” Patty asked.

“It’s pretty consistent with what Dave’s PowerPoint® slide shows", Sally answered.  "His result is for one of our six lines.  We are using the new solder paste on all of the lines now and profitability is up about 8%, or more than $6 million for a year.”

“A lot of the added profit is from cost savings that purchasing has implemented,” Blaine shot in.

“You don’t realize the pressure I am under to reduce the cost of purchased goods.  Components, PWBs, connectors, solder paste, flux, packaging, etc, is over 80% of all of our total cost. Corporate has been all over me because of the increase in solder paste cost,” Ellis went on in frustration.

“Part of the increased cost of solder paste is because we ship more product, we actually use less paste per board with the new paste,” Dave responded.

“How so?” asked Sally.

“The old paste had poor response to pause.  If we stopped the line for a few minutes, the first one or two prints afterward would be poor because the paste stiffened up.  We would have to wipe the paste off those boards and reprint them.  This would happen a couple of times per day.  The ProfitPro™ output shows the increased productivity and profitability for the line for which I am responsible. Note that the profits are up $841K!” Dave Ferris went on.

“But my purchasing expenses have gone through the roof!” Blaine Ellis blurted as he stormed out of the room.

Patty, Pete, Dave, and Sally, sat there dumbfounded, looking at each other.

Pete finally spoke up, “Let me go talk to Blaine,” he said as he left the room.

“One of the issues is that Mr. Ellis should not be criticized if a consumable costs more money if it increases profitability. That doesn’t make sense,” Patty said.

“I agree” said Sally, “But much of the pressure comes from ‘Corporate.’”

As Sally was speaking, it occurred to Patty, that, in her new role, she may be able to impact this ineffective corporate policy.  As she was mulling over this thought, Pete and Blaine Ellis returned to the room.

Ellis spoke first.

“After discussing the situation with Pete, it occurs to me that young Ferris’s profitability argument may have merit,” Ellis started.

“But Dr. Coleman, I need your help,” Ellis implored.

At this Patty’s ears perked up.  First, she is not used to being called by her last name and second, she was unaware that she had a PhD!

“I think I know what you need,” Patty responded.  “We need to change the corporate criteria for evaluating the effectiveness of purchasing, to include situations like this.  I’m quite sure I can do it,” Patty finished cheerfully.

The meeting concluded with all agreeing that Dave Ferris should be given the corporate award and Patty reaffirming her commitment to change the corporate policy.

In several hours, Patty and Pete were on an airplane heading home.

"OK, out with it," Patty teased Pete.

"What?" was Pete's sheepish reply.

"How did you know Blaine?" Patty asked.

"Remember, when I told you that I tried out for Olympic volleyball years ago?" Pete responded.

"Yes, " Patty replied.

"So did Blaine. I'm not sure which one of us was more humbled by the experience," Pete chuckled.

Cheers,

Dr. Ron

Folks,

It has been a while, let's look in on Patty......

Patty had to admit that she was very fortunate.  She had yet to turn 30 and she was a Senior Vice President at ACME.  There was even a small article about her in Fortune magazine.  But she had to admit that, at some level, she was bored.  She missed the action of being out on the line and solving problems. 

With these thoughts she headed toward the lunch room.  She had avoided eating lunch with the execs and still ate lunch with the young engineers that were her age. No one thought it strange.  Pete was occasionally the old timer in the group, as he was approaching 45 years old. 

As she sat at lunch with her friends, Patty also had to admit that she was jealous of all of the group's talk about solving technical problems.  She was now responsible for corporate strategies and seldom got her “hands dirty.”  So she missed the technical challenges on the shop floor. 

After lunch she stopped Pete.

“Hey, Pete, could you stop by my office?” Patty asked.

“Kiddo, for you anything….even that,” he answered and they both chuckled.

As Pete sat down in Patty’s office, she asked him, “How do you like your new job?”

“What’s not to like? Twice as much money and working with you!” Pete answered.

“But don’t you miss ... ,” Patty stopped and struggled to gain her composure.

Peter helped her, “Working on the shop floor solving process problems?”

“Yes, so much so that I could almost cry,” Pete finished.

They were silent for awhile.

Then Pete suggested, “Why don’t I see if I can find us a problem.”

Patty smiled. Pete was always well connected.

A few days passed and Patty had just about forgotten about their meeting.  There was a knock on her door and Pete stuck his head in.

“Hey kiddo, we have an assignment,” Pete shouted cheerfully.

Patty perked right up.

“What’s the scoop?” she asked.

“You know the new program that rewards cost savings?” Pete asked.

“Sure, I think it is a great idea,” Patty responded.

“There is a conflict in our plant in Santa Clara. Management wants to give a $10,000 reward and the senior purchase manager is blocking it,” Pete elaborated.

“Why?’ Patty asked.

“The engineer deserving of the reward purchased a solder paste that improved uptime,” Pete said.

“Sounds great, what is the issue?" Patty asked. "Let me guess. The better solder paste costs more?” she asked.

“Yep!” Pete responded, “One penny per gram.”

“Mike Madigan wants someone to negotiate the situation. Why not us?” Pete asked.

Patty quickly sent Mike an email offering to help.  He gave her the go ahead shortly thereafter.

In a matter of days the arrangements were made and Patty and Pete were on a jet from Boston’s Logan airport to San Jose, California. 

Their flight had taken off and they were enjoying a snack, when Pete commented, “Let’s hope we don’t find someone there like the guy who wanted to assemble the boards without the boards,” Pete chuckled.

At this comment, Patty almost choked on her sparkling water.  About four years ago, when Patty was just starting out, they were working on a critical project.  The manager in charge wanted the boards to be assembled on a certain date.  Unfortunately, the PWBs did not arrive on time, even though all other components, connectors, and the other hardware where ready.  The manager, in frustration, came out to the line on the scheduled start date and was furious that the boards were not being assembled.

The manager asked the lead engineer, “Why aren’t the boards being assembled?”

The lead engineer responded, “The PWBs did not arrive from the vendor.”

To this the manager responded, “Aren’t you going to assemble them anyway?” (See note below.*)

This was their favorite story about the occasional comedy in electronics assembly.

It seemed like no time at all and Patty and Pete were sitting in the conference room that had been reserved for the meeting.  They introduced themselves to a young engineer who was sitting in the room waiting for the meeting to start. His name was Dave Ferris.

“So Dave, you are the cause of this meeting, eh?” Pete teased.

“I guess so. I can’t believe how hard it is to sell productivity here.  The amount of time the new solder paste saves enables us to produce 1,000 more units per year on each line. And these boards are super expensive, with high margins.   Admittedly the solder paste costs $0.01 more per gram, but the additional profit is over $800,000 per year for each of our three lines,” Dave Ferris explained.

“How did you perform the calculations,” Patty asked.

“I went to a workshop run by this quirky, cheerful guy everyone calls ‘The Professor.’ He was amazing,” Ferris replied.

Pete and Patty both chuckled.

“We know The Professor well,” they chimed in unison.

“We assume you used “ProfitPro™ for the calculations?” Pete asked.

“Yes,” Dave responded with a surprise in his voice that they would know about such things.

Will Patty and Pete save the day?  Will Dave get his award?  Stay tuned to see.

Cheers,

Dr. Ron

*As hard as it is to believe, the story about building the boards without the PWBs is true.  Thanks to ITM.

Folks,

This year will mark the 10^th anniversary of SMTA’s SMT Process Certification Course.  This course was developed by Jim Hall, Phil Zarrow and me, Dr. Ron Lasky, in time for SMTAI 2002.  The workshop covers all of SMT from materials (strong emphasis on solder paste), components, soldering, stencil printing, component placement, reflow soldering, wave soldering, dispensing, test, and more.

The course assumes that the material is mostly review, so it is definitely not introductory level.   At the end of the workshop are an open book and closed book test.  Upon passing the test, you will have the privilege of adding the title: “Certified SMT Process Engineer*” to your business card.

Among the many benefits of SMTAI certification is being able to inform your customers that your product is manufactured by credentialed personnel. Since the SMT Process Certification Course is taught internationally, this credential is recognized globally.

So if you, or your team, are not already certified, join the tenth anniversary workshop at SMTAI 2012  at Disney World and get certified!

Cheers,

Dr. Ron

* In some countries, such as Canada, the term "engineer" has legal implications and cannot be used unless the person has a degree from an accredited university or a Professional Engineer's License.

Folks,

It is hard to believe that I have been blogging for over 7 years now.  In all this time it has surprised me how much interest there has been in the solder density calculator that I developed.  At the suggestion of Tim Jensen, I have added a feature that can calculate the volume of solder paste and flux if given their masses or vice versus.  The densities of the solder paste alloy and flux are also needed.  Most fluxes have a density of about 1 g/cm³.   If you are interested in this updated software tool, download it here.

Knowing the volume of the solder and flux in a solder paste is critical if you are using the Pin-in-Paste process, with or without solder preforms.  I have also developed a software package called StencilCoach™ that can calculate stencil parameters and the special parameters needed for the Pin-in-Paste process.  I will also send this free software tool to those that are interested.

The image shows the schematic for the solder volume calculations for the Pin-in-Paste Process.  The equations were developed by Creyr Innovation’s Jim McLenaghan.

Cheers,

Dr. Ron