Month: May 2021

If you didn’t catch the previous parts of this series on what electronic tests are and how they relate to sustainability, you can find the first part here! (No really, you’ll be very confused if you read this article otherwise 😁).

But as quick refresher: improved technology is the key to sustainability–from energy-efficient LEDs to environmentally-friendly batteries! But many tests are needed to improve technology and these should look for ‘failure modes’ (the events that lead to electronics breaking), using tests that speed up both burn-in and lifetime under different (usually harsh) conditions. For example, you might run a burn-in test for an integrated circuit chip (computer chip) at 130°C instead of 25°C (even though most computers are probably stored at room temperature instead of 130°C).

There is now a variety of tests used by the industry to test the durability and longevity of their products, such as: Highly Accelerated Stress Test (HAST); Highly Accelerated Lifetime Test (HALT); High Temperature Operating Life (HTOL) Test; Accelerated Lifetime Test, etc. All these tests follow the same principle: the worse the conditions, the faster the degradation. The issue is figuring out how to balance the test accuracy, making sure not to create unrealistic conditions that may not apply to reality. For example, solar panels need to absorb sunlight, and for higher efficiency they need to reflect very little sunlight. To achieve that solar panels must be treated with special antireflective coatings. The coating under intense heat should be able to perform, but at the same time it has to deal with the pressures of cracking, atomic defects, rain, snow, and… bird poo!! How do you go about including all those things in your testing standard??? This is why testing standard organisations exist, like the International Standards Organisation (ISO), the International Electrotechnical Commission (IEC), the Deutsches Institut für Normung (DIN). These organisations have hundreds of committees with experts who have decades of industry experience that decide the specific test requirements for any electronic part (from batteries to supercapacitors to hard drives and beyond).

Keep in mind that these standards aren’t regulations. Manufacturers just use them to show their products’ quality. But test are costly, and as a result manufacturers use some sort of statistical/simulation-based tools to predict the test outcome early. These types of tools are called ‘prognostics’. Many companies are now developing prognostics to estimate the health of electric vehicle batteries after they’ve been in use. This is especially used when products reach the end of their first lifecycle, in order to predict the possibility and potential efficiency of entering into a second lifecycle (e.g. reusing batteries, or parts of bulky electrical equipment such as laundry machines or fridges).

Prognostic tests can be based on statistical simulations. These statistical models find relationships between data and can extrapolate specific relationships between variables, analyse special statistical distributions like the Weibull distribution, and even use machine learning techniques to classify and forecast data. Still, not all tests and electronics will be ‘fixable’ with statistical prognostics alone. For example, some types of electronic products don’t have ANY failure modes that show up for a certain period of time (longer than 100 hours) (e.g., electrical grid components, like insulated transformers / power cables that often withhold their performance for YEARS). Though burn-in tests for 100 hours would catch defective units for many types of electronics, this means some types of important sustainable technology would need even longer burn-in tests.

This pretty much eliminates any type of statistical model where you want to collect some data and then extrapolate a curve of fit forward to predict results.

There’s also another issue with using statistical prognostics to develop sustainable electronics faster. Some electronics won’t have quantifiable data that you can use for statistical modelling solutions. Two reasons for this are that quantifiable test standards haven’t been developed or the electronic product is too complicated.

For instance, test standards for optical coatings in fibre-optic networks (ie. high-speed Internet) have only been around for about 45 years. Coatings are tested to make sure they can resist scratches and don’t easily come off things they’re applied to (like mirrors and lenses). These tests take hundreds to thousands of hours and some parts are quantifiable (ex. 1000 hours at 85°C and 85% humidity). But other parts are just mere ‘visual’ checks: rub a cheesecloth against a coating 50 times and see if you have scratches. It’s hard to gather data about the effect of cheesecloth rubs though. So statistical models aren’t easy to build here.

In contrast, integrated circuit chips (computer chips) have had testing standards since the 1970s. But each chip has billions of transistors (electronic switches to let current pass through), capacitors (electronic devices to store static charge), and other components. How do you test if one of them is broken? No magical engineering solutions here, unfortunately. Instead, engineers create what are called ‘test patterns’. These are basically different input, output pairs to test on the computer chip. If you input data and get the expected output, the individual computer chip is working. For example, you could write data to the computer chip and then read it back. If the data you read isn’t what you tried to write, you know the computer chip didn’t save the data properly.

BUT data saved isn’t really a ‘physics’ variable that we can quantifiably test like with other electronics. And even if we tried to test variables like temperature, current, etc. — which of the billions of components in a chip do you take readings from?? This is why statistical models are also hard to build here.

In both cases, though, there is an option for carrying out measurements. Instead of collecting data from optical coatings or computer chips, you collect data from the machines that make them. And you use different data from the factory production environment to optimise the production process and MAYBE make some predictions about the functionality of individual batches of electronics. There are companies working on this very complicated approach. But especially with newer sustainable technology (ex: new types of energy storage electronics or energy production electronics), there’s a lot of potential for new growth!

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Certainly, there’s a lot of innovation to be had in creating more sustainable technology for the future… but better testing technology is the pre-requisite to unlocking that innovation!

If you have any questions about this article, feel free to email Voltx’s cofounders: Alishba Imran or Shagun Maheshwari!

Thank you to: 🙏

• Dr. Jeff Jones from the IEC. I wouldn’t have understood the connections between different electronic products without you!
• Dr. Darayus Patel from the Nanyang Technological University. I’m grateful for all your enthusiastic support in breaking down semiconductor fabrication with me!
• Dr. Stefaan Vandendriessche from Edmund Optics. I couldn’t have imagined the issues with testing optical coatings without your tip!
• Dr. Robert Herrick from Intel. I’m amazed by all your selfless support in answering my endless questions about the optoelectronics industry!

Written by Madhav Malhotra, a 17-year-old developer, designer, and entrepreneur-in-training. To find out more about the author, please visit https://www.madhavmalhotra.com/

If you didn’t catch the previous parts of this series on what electronic tests are and how they relate to sustainability, you can find the first part here! (No really, otherwise you’ll be very confused if you read this article otherwise 😁).

To briefly summarise: improved technology is the key to sustainability–from energy-efficient LEDs to environmentally-friendly batteries! But many tests are needed to improve technology and they look for complicated ‘failure modes’ (the events that lead to electronics breaking).

Given all these unique types of tests, why did I claim up above that ‘EVERY’ electronic has slow testing? Well, ‘EVERY’ electronic from tiny batteries to giant power cables has two important (but slow) types of tests: accelerated lifetime testing and burn-in testing. As I explained in the first article, these tests are often done by manipulating the exact same variables: temperature, humidity, current, and/or voltage higher and performance over time.

To make it less theoretical, I’ll talk about lifetime testing.

Let’s say you’re a brilliant, laser in a LiDAR sensors that many automated electric vehicles rely on to enable cleaner transportation. You are about to be tested to see if you work! You will be subjected to a 75°C temperature (with all your other laser friends) and see how long it takes you to break. Here’s what I’d see:

This is a curve like many others in electronics testing. It shows how variable X increases and/or decreases as the time variable increases and a device ages. Basically, as you shine longer, you age more and get tired. You need more ‘fuel’ (i.e., current) to keep you going. Eventually, you’ll need so much ‘fuel’ that you just won’t meet the user’s laser shining needs (and these needs can be important to the user, and can be detrimental from an environmental, economic and social perspective)! Even though, from a technical perspective, you may still be functional you may not offer the desired qualities to the end-user.

Different lasers, have different performance. As shown in Figure 1 lasers indicated with a maroon, purple, and pink colours have a 1000+ hours performance, and it is uncertain as to how long it’ll take for their fuel needs to increase considerably. The Arrhenius equation is often used to figure out what the laser lifetime would be at regular temperatures, given what we know about their lifetime at high temperatures. Warning… CHEMISTRY ahead. Do you remember this monstrosity from high school???

This is the Arrhenius Equation, and if you haven’t seen it since your high school chemistry class, fear not! Here’s the oversimplified version:

• k is how fast atoms move.
• T is the temperature.
• k is equal to a complicated mess that we don’t care about. We just need to know k (how fast atoms move) increases as T (temperature) increases.

This allows us to model how fast atoms move at different temperatures (among other things). And remember what I said in the second article in this series: The faster atoms move the faster electronics degrade. So, the Arrhenius equation can be used to model how long it will take for electronics to degrade at different temperatures: Life is inversely proportional to how fast atoms move (how naughty, and chaotic their life can become), which is complicated to calculate.

In fact, this is exactly why we go to such great lengths to add heat sinks or cooling systems to devices. Cool right? Based on chemistry, and a gallop of physics and math we can model how sustainable technologies like, electric vehicles, solar panels, wind mills, would last while being subjected to environmental conditions (e.g. radiation, heat, humidity and wind) for many years.

Now, what does laser Figure 1 tell us about lasers’ performance before the 100 hours pass? That’s where the burn-in testing happens (e.g. every laser is run for 100 hours to find any defects). The issue is that many failure modes for electronics happen ONLY in that first little while of testing (it’s like you only get chickenpox once— usually when you’re young). But for lasers, it’s more like the atoms in the crystal structure ‘dislocate/diffuse’ to the wrong places when they’re young. The problem with that is that it’s pretty hard to predict all those specific one-time failure modes. Burn-in testing is hard to extrapolate which makes it even harder to prevent potential lower quality electronics to be placed on the market.

EVERY product needs lifetime testing and EVERY unit of every product needs burn-in testing for sustainable electronics and electrical equipment to be placed on the market. Even though, there are considerable ‘costs’ (e.g. energy, carbon emissions, operational and maintenance costs) involved in testing millions of electronic and electrical equipment made at factory, these could be significantly lower than the ‘costs’ associated with electronics and electrical equipment that becomes easily obsolete, especially considering the fact that these become waste very quickly and are alarmingly mismanaged. An in-depth system-based sustainability assessment is needed to demonstrate the impacts involved!

By this point in the series, I hope you realise how important technology innovation and testing is for achieving sustainability in the long-term. With this in mind, I’ll finally wrap up the next (and last) part of this series by looking at solutions to all the major problems I’ve highlighted so far! 🛠️💪

Written by Madhav Malhotra, a 17-year-old developer, designer, and entrepreneur-in-training. To find out more about the author, please visit https://www.madhavmalhotra.com/

If you didn’t catch the first part of this series on what electronic tests are and how they relate to sustainability, you can find it here! To summarise: there are different types of electronics tests at different parts of the electronics’ life (e.g. initial design vs. end use). They help us improve technology faster. And improved technology is the key to sustainability–from energy-efficient LEDs to environmentally-friendly batteries!

If you’ve read the first article in this series, you might have noticed how a LOT of tests (for everything from hard drives to solar panels to lasers) involves heating them up. It’s one of the most common variables controlled in electronics testing. Why is that? Basically, modern electronics are made with very SPECIFIC chemistry. All the atoms have to be in just the right place (and the places have weird names, like ‘p-n junction’). At higher temperatures, atoms get ‘excited’ and move about more and more. Eventually, they get so ‘excited’ they move away from the places we want them to be. This ‘naughty’, uncontrolled behaviour causes electronics to break. Which is why temperature is an important variable in all tests, and why electronics have cooling fans, heat sinks, etc. in practical applications. 

🔑 FACT: for every 10° C increase in temperature, an electronic can break twice as fast.

Other common variables that can affect the lifetime of many types of electronics are the current and voltage they receive. Current is the flow of electrons, resistance slows down the flow, and voltage speeds up the flow.

Alongside these basic variables measured, there are some that are more specific to the electronic being measured. For instance, batteries measure a variable called self-discharge when they age, which is when a battery loses energy without being plugged in. Over time, this decreases the amount of energy the battery has stored. And this measure of aging is becoming more important with electric cars. You wouldn’t want to buy an electric car if you didn’t know how many years it would take before you’d have to buy a new battery. Right?

Behind all these variables is a complicated physics. It has to do with two key terms: failure modes (events that break an electronic); and failure mechanisms (the causes behind those events). Physics variables can measure when a failure mode occurs. For example, a power cable that transmits electricity underwater would undergo corrosion (failure mechanism). Eventually, there would be so much corrosion that the cable would snap (failure mode). And we would detect the cable snapping when current is no longer flowing through the wire (variable).

Though we can use current to monitor failure modes of power cables just like we can use it for laser diodes, there are VERY different failure modes for the two electronics. This is the in-depth explanation of something I mentioned in the first article; it’s REALLY hard to test electronics once they’re deployed in the field. For every electronic you want to test, you have to consider ALL the environmental conditions that could trigger ANY of the unique failure modes for that electronic. And you have to simulate the physics of the situation to understand when the failure mode might be triggered!

I hope you now see why electronics testing is a lot more complicated than it first seems. THIS is the complexity that delays electronics testing and slows down rapid innovation for future sustainable technology. In the next part of the series, I’ll describe the SPECIFIC parts of electronics testing which are slow (in case any of you sustainability innovators have ideas for how to speed up the development of green, new technology)!

Written by Madhav Malhotra, a 17-year-old developer, designer, and entrepreneur-in-training. To find out more about the author, please visit https://www.madhavmalhotra.com/