Issue link: https://iconnect007.uberflip.com/i/1528798
NOVEMBER 2024 I SMT007 MAGAZINE 29 use case we are doing with this data in collect- ing every component. Not only are you executing the black-and- white decision between legitimate and counterfeit, but you're also tracking a finer-grained consistency. Counterfeit is rare, especially when you work with the biggest ones. Why? Companies like NVIDIA are not buying from the open market, but from franchise distributors or direct from the manufacturer. What are the chances that some franchises will provide them with coun- terfeit components? That would be very rare. So, the risk is not there, but these same people would like to make sure that the AVL is being enforced. It's a more advanced perspective— not just counterfeit detection but a broader perspective. I've seen in Cybord's development work that counterfeit detection is easy. It happens at incoming inspection: The part is good, or it's not. What opportunities has Cybord been putting into the middle and end of the man- ufacturing process to find counterfeits and improve quality through your manufacturing supply chain? Our soware integrates with existing equip- ment, and this is one of our main benefits. We connect to the pick-and-place machines at the beginning of the assembly process and to the AOI machines at the end. From this, we get two perspectives: e pick-and-place provides the bottom-side view of all the components, and the AOI provides us with the top-side information. With this, we cover all the com- ponents on both sides. Looking from the bottom, you can recognize defects, cracks, broken parts, bent leads, and contaminations. You can see counterfeit com- ponents mainly from the bottom, not from the top. e top side includes the top marking, which is easy to fake. From the bottom side, we see the leads, their coverage, the shape of the corners, and the texture. is is very hard to counterfeit, so the bottom side is the right place to look for counterfeit details. You're creating a lot of data through the whole process, so how do we make sense of that data? Let's say that you're a procurement guy, and you are paying for one manufacturer. You pay for a capacitor at 3x the lowest competi- tor because you think they are better quality. Cybord thinks these assumptions shouldn't matter anymore. You have data to use; if you compare the so-called high-quality supplier with the second-tier supplier, you can compare the two and make sure that paying 3x is worth it. Most of the time, I can tell you it's not. But let's look at the actual data, which is an impor- tant analysis that couldn't happen before. We can do it now because they have the data. In another example, a customer says, "I have a small board and I'm struggling to place all the small components because the density is just unbelievable." Now, the problem is that every supplier pro- vides spec limits. For every component, we Oshri Cohen