One of the great challenges of tin whiskers is detecting them. When one considers that their median thickness is in the 3 to 5 micron range (a human hair is about 75 microns,) they can be hard to see with direct lighting. Right angle lighting facilitates visual detection. See Figure 1. In this figure, Panashchenko shows that with direct light (left image), it is impossible to see the tin whisker, however with right angle light the tin whisker jumps out.
Figure 1.* It is not possible to see the tin whisker with direct lighting as in the left image. However, in the right image, right angle lighting makes it easy to see the tin whisker.
Start with low magnification and work up to high magnification
Have the ability to tilt the sample in 3 axes
Use a flexible lamp that allows multiple angles of illumination, do not use a ring light
Use a LED or fiber optic lighting, not incandescent lights which can cause shadowing
Vary the brightness of the light source
The most important tip is to vary the angle of lighting while varying the magnification. Thus, analyzing a sample should take several minutes, at least. However, even the most thorough inspection may miss some tin whiskers.
In the next post, I will discuss mitigation techniques.
*The image is from Lyudmila Panashchenko, “The Art of Metal Whisker Detection: A Practical Guide for Electronics Professionals,” IPC Tin Whisker Conference, April 2012.
“India officials are allegedly subsidizing US$10 billion in semiconductor manufacturing, according to Reuters citing knowledgeable sources.”
A quick review of Reuters stories over the past 90 days show no such reporting, however.
Is DigiTimes wrong?
Nope. But one must go back to Mar. 31 to find the piece: “India is offering more than $1 billion in cash to each semiconductor company that sets up manufacturing units in the country as it seeks to build on its smartphone assembly industry and strengthen its electronics supply chain, two officials said.”
This happens a lot, actually. I got a kick out of a recent recycling by multiple industry news aggregators that claimed Epec has acquired NetVia.
“Hmmm,” I thought. “That’s weird.” Because I am pretty confident that already happened.
What happens is that aggregators use alerts to find news, and crawlers sometimes bring old information back to the surface. Unsuspecting or inattentive editors grab the “new story” and link to it for that day’s newsletter.
Continuing our series on tin whiskers. In thelast post we discussed what they are. in this post we will discuss what causes them.
Tin whiskers are primarily caused by compressive stresses in tin. The most common cause of the stresses is copper diffusion into the tin as seen in Figure 1a. Such diffusion is common when tin is plated, melted or evaporated on copper. Copper preferentially diffuses into tin exacerbating tin whisker production.
Figure 1. Some causes of tin whiskers
Another cause of tin whiskers can occur when the tin is plated, melted or evaporated on a material that has a lower coefficient of expansion than the tin, such as alloy 42 or ceramic. When temperature increases, the tin is constrained by the lower coefficient of expansion material. This constraint causes compressive stresses in the tin that can result in tin whiskers. See Figure 1b.
Less common causes are corrosion, as seen in Figure 1c and mechanical stresses as seen in Figure 1d.
Since copper diffusion is one of the most likely causes of tin whiskers, this mechanism deserves elaboration. The left image in Figure 2 depicts the mechanism of copper diffusion into tin. The mechanism is so strong that the diffusion of the copper often leaves voids in the copper. Such voids are called Kirkendall voids. The right image in Figure 2 is an x-ray map of copper (green) diffusing into the tin (black).
Figure 2. Copper diffusing into tin.
Clearly, one way to minimize this type of tin whisker growth is to prevent copper diffusing into tin. In a future post, we will discuss this and other tin whisker mitigation techniques.
Tin whiskers are very fine filaments or whiskers of tin that form out of the surface of the tin. See Figure 1. They are the result of stress release in the tin. Tin whiskers are a phenomenon that is surprising when first encountered, as their formation just doesn’t seem intuitive.
They are a concern, as they can cause electrical short circuits or intermittent short circuits as a fusible link. Lead in tin-lead solder greatly suppresses tin whisker growth. Therefore, with the advent of lead-free solders there is a justifiable concern for decreasing reliability due to tin whisker growth in electronics.
Tin whiskers can vary in length and width, as is seen in Figure 2. Note that although only about 10% are as long a 1000 microns (1mm). That length and occurrence rate is such as to cause many reliability concerns.
Figure 2. The length and width of some tin whiskers. The source is also the NASA Tin Whisker Website.
Over the following weeks I plan to post how tin whiskers form and strategies to alleviate them. Most of the information I will post comes from a paper I presented with Annaka Balch at the SMTA PanPac 2019.
NASA has an excellent website that provides much information about tin whiskers and is a source for historic critical failures caused by tin whiskers.
It’s always interesting when the seers, also known as the industry’s journalists, get together for a chat.
Hosted by Mike Konrad, I joined Trevor Galbraith, Phil Stoten and Eric Miscoll to discuss post-pandemic production, innovations in our industry, supply chain and labor shortages, and of course, some predictions.
Manufacturers are increasingly evaluating the environmental effects of their practices as eco-consumerism becomes more widespread. Companies can increase their profits by making their product development more sustainable and supporting energy-efficient infrastructure development.
President Biden allocated trillions of dollars to the sustainability sector, promoting low emission production. Reaching carbon neutrality requires a restructuring of the electronic manufacturing sector. Before evaluating the impact of reduction methods, we must examine the degradation associated with commonly used devices.
Life Cycle Assessment
A significant portion of electronic development derives from material mining. Many devices contain lithium-ion batteries, linking manufacturing processes to ecological degradation. Inadequately maintained mining sitespollute local water supplies, like the Liqi River in Tibet. Here, a lithium mine generated a chemical leak, killing a significant quantity of marine life.
Mining also depletes local water sources in drought-ridden regions. Lithium derives from a saltwater brine, which workers extract. Over time, local farming operations suffer from low groundwater levels. The extraction process also pollutes the air, causing adverse health effects.
Inefficient manufacturing processes can also generate pollution by developing electronic waste. The U.S. produces the most e-waste in the world. Chemicals leach into the soil from electronic landfills, which degrades the environment and human health. These dumps contain lead, mercury, cadmium and more,polluting food sources and drinking water. They also contain persistent organic compounds from fire retardants. When consumed, the substances cause cognitive defects in children and behavior or motor skill challenges.
Another environmentally degrading factor of production derives from energy use. China manufactures the highest portion of electronics globally. Coal isChina’s largest energy source, fueling many production facilities.
When products leave the center, they absorb a portion of the emissions generated. Fortunately, manufacturing facilities can increase the sustainability of their products by using renewable energy sources. Over time, their environmental impact will decrease.
Renewable Energy Sources
Large corporations recently adopted renewable energy systems in production, meeting eco-consumerism demands. Over the past year and a half, BMW used solar and wind power todecrease the emissions generated by its manufacturing facilities. It also increased the energy efficiency of their products, shrinking their carbon footprint throughout their life cycle.
If China’s electronic manufacturing facilities converted from coal-powered electricity toward renewable energy, they could significantly increase their practices’ sustainability. Reducing greenhouse gas emissions and adding energy-efficient appliances can decrease a company’s carbon footprint.
Some companies decrease their production facilities’ ecological impacts by swapping conventional lights with light-emitting diode (LED) bulbs. Thebulbs absorb 75% less energy than incandescent lights and last 25 times longer.
Improve Product Longevity
The best way to target e-waste is through improving products’ longevity. Some companies utilize planned obsolescence to maintain a consistent revenue stream. The expiration date on electronics increases e-waste production and decreases their sustainability.
Some electronics companies source ceramic and glass for product development. The materials have a limited defense against electronic stressors and generate pollution over time. Replacing the materials with liquid silicone rubber can make a product last longer and eliminate the normalization of planned obsolescence.
Manufacturers can also sustain economic gains by increasing the price of long-lasting products. Customers are more likely topurchase sustainable goods over their less expensive counterparts. If we build products to last, it increases profitability while decreasing environmental impact.
Rather than mining lithium-ion battery elements each time we produce new electronics, we can utilize recycled materials. Environmental engineers and scientists are generating efficient lithium-ion recycling technology, extracting functional features from the devices. The Department of Energy (DOE) developed the first recycling center, increasing the industry’s profitability.
The DOE also developed a program influencing professionals to develop advanced lithium-ion recycling technology. It offered thewinner a $5.5 million reward to expand the system’s efficiency. When using recycled materials, manufacturers can reduce their reliance on ecologically degrading mining practices.
Where to Start
Manufacturers can begin decreasing the environmental impact of their practices by leaving fossil fuel-derived electricity sources behind. Renewable energy is abundant and currently cost-effective, improving sustainability rates while reducing utility costs. It also helps companies immediately reduce greenhouse gas emissions, shrinking their carbon footprint and making them more appealing to a new generation of consumers.
Jane Marsh is the founder and editor-in-chief of Environment.co where she covers topics in green technology, energy and environmental sustainability.
The EMS industry has posted several straight months of what some consider excessively high book-to-bill ratios. The April peak of 1.62 has only marginally fallen over the past couple months and, as of this writing, was 1.48 in June, the most recent data available.
As a refresher, the ratio is calculated by dividing the amount (in dollars) of bookings by the amount in shipments. In other words, if over a set time period a company gets $110 worth of orders and ships $100 worth of product, its book-to-bill ratio would be 1.10. A ratio over 1.0 is considered an indicator of future market growth.
So a positive ratio is a good sign, generally speaking, but too much of a good thing makes folks nervous. And ratios in the 1.40 and above range are historically at the high end.
Some are concerned of an overheated market, but conversations with several leading EMS firms suggest instead that OEMs are offering longer forecasts, which are inflating the numerator. For instance, if the typical window was six months, it might be nine or even 12 months now. That pushes more “orders” into the data pile, but it’s a mathematical anomaly, not a sign of double-booking.
I don’t expect the sky to fall, at least this time.
I am reposting an updated blog post on Cp and Cpk calculations with Excel, as I have improved the Excel spreadsheet. If you would like the new spreadsheet, send me an email at email@example.com.
One of the best metrics to determine the quality of data is Cpk. So, I developed an Excel spreadsheet that calculates and compares Cps and Cpks.
It is accepted as fact by everyone that I know that 2/3 of all SMT defects can be traced back to the stencil printing process. A number of us have tried to find a reference for this posit, with no success. If any reader knows of one, please let me know. Assuming this adage is true, the right amount of solder paste, squarely printed on the pad, is a profoundly important metric.
In light of this perspective, some time ago, I wrote a post on calculating the confidence interval of the Cpk of the transfer efficiency in stencil printing. As a reminder, transfer efficiency is the ratio of the volume of the solder paste deposit divided by the volume of the stencil aperture. See Figure 1. Typically the goal would be 100% with upper and lower specs being 150% and 50% respectively.
Figure 1. The transfer efficiency in stencil printing is the volume of the solder paste deposit divided by the volume of the stencil aperture. Typically 100% is the goal.
I chose Cpk as the best metric to evaluate stencil printing transfer efficiency as it incorporates both the average and the standard deviation (i.e. the “spread”). Figure 2 shows the distribution for paste A, which has a good Cpk as its data are centered between the specifications and has a sharp distribution, whereas paste B’s distribution is not centered between the specs and the distribution is broad.
Figure 2. Paste A has the better transfer efficiency as its data are centered between the upper and lower specs, and it has a sharper distribution.
Recently, I decided to develop the math to produce an Excel® spreadsheet that would perform hypothesis tests of Cpks. As far as I know, this has never been done before.
A hypothesis test might look something like the following. The null hypothesis (Ho) would be that the Cpk of the transfer efficiency is 1.00. The alternative hypothesis, H1, could be that the Cpk is not equal to 1.00. H1 could also be that H1 was less than or greater than 1.00.
As an example, let’s say that you want the Cpk of the transfer efficiency to be 1.00. You analyze 1000 prints and get a Cpk of 0.98. Is all lost? Not necessarily. Since this was a statistical sampling, you should perform a hypothesis test. See Figure 3. In cell B16, the Cpk = 0.98 was entered; in cell B17, the sample size n = 1000 is entered; and in cell B18, the null hypothesis: Cpk = 1.00 is entered. Cell B21 shows that the null hypothesis cannot be rejected as false as the alternative hypothesis is false. So, we cannot say statistically that the Cpk is not equal to 1.00.
Figure 3. A Cpk = 0.98 is statistically the same as a Cpk of 1.00 as the null hypothesis, Ho, cannot be rejected.
How much different from 1.00 would the Cpk have to be in this 1000 sample example to say that it is statistically not equal to 1.00? Figure 4 shows us that the Cpk would have to be 0.95 (or 1.05) to be statistically different from 1.00.
Figure 4. If the Cpk is only 0.95, the Cpk is statistically different from a Cpk = 1.00.
The spreadsheet will also calculate Cps and Cpks from process data. See Figure 5. The user enters the upper and lower specification limits (USL, LSL) in the blue cells as shown. Typically the USL will be 150% and the LSL 50% for TEs. The average and standard deviation are also added in the blue cells as shown. The spreadsheet calculates the Cp, Cpk, number of defects, defects per million and the process sigma level as seen in the gray cells. By entering the defect level (see the blue cell), the Cpk and process sigma can also be calculated.
Figure 5. Cps and Cpks calculated from process data.
The spreadsheet can also calculate 95% confidence intervals on Cpks and compare two Cpks to determine if they are statistically different at greater than 95% confidence. See Figure 6. The Cpks and sample sizes are entered into the blue cells and the confidence intervals are shown in the gray cells. Note that the statistical comparison of the two cells is shown to the right of Figure 6.
Figure 6. Cpk Confidence Intervals and Cpk comparisons can be calculated with the spreadsheet.
This spreadsheet should be useful to those who are interested in monitoring transfer efficiency Cpks to reduce end-of-line soldering defects. It is not limited to calculating Cps and Cpks of TE, but can be used for any Cps and Cpks. I will send a copy of this spreadsheet to readers who are interested. If you would like one, send me an email request at firstname.lastname@example.org.