![]() That getting that high yield, of course, maps directly into the cost of these chips, cause if you if you make one of these chips in a foundry or a factory, if you make a wafer full of them, these are these round discs where they manufacture the chips. Well, it’s very difficult to get all those 8.5 billion transistors to yield and have billions and billions of those chips come out. People are wanting to build foundries all over the world, not just in in certain parts of the world. The thing about the failed devices is that as they ramp a new process technology in one of these foundries, you may have heard it recently in the news. And so far I haven’t talked about the machine learning cause it gets, the machine learning is really useful to us when we talk about yield and trying to understand the chips that actually fail those tests. You test it at a high level of quality at low cost. Matt: So our group does is we make software that inserts circuitry into that chip so that it’s easier to test. You don’t want it to last more or less than a week, right? You want to last couple of years at least. ![]() Because of course you don’t want your cell phone to die on you. Matt: Because you have to do it quickly and you have to do it at very fast and have a low cost. Now in reality it’s very, very difficult to test every one of those 8.5 billion transistors. Yet they have to be tested, and they have to be 100% performative so that you can make your phone calls, watch your videos and do your social media. Well, these chips are the most complex things ever made by mankind. These are these little switches of ones and zeros that help keep us all connected. You know it’s the size of a thumbnail but it’s got 8.5 billion transistors. So if you look at a typical semiconductor chip today, this is a chip that’s probably in your cell phone. Matt: Sure, it’s a little bit of a long story, but let me start with just talking about the semiconductor industry and some of the complexities that we have to deal with. So can you tell us how about the relationship between EDA and semiconductor design and artificial intelligence or machine learning? Today I am joined by an expert from Siemens EDA with more than 20 years experience to discuss the ways Tessent uses artificial intelligence and machine learning to detect defects and improve yields in microchips. ![]() In this series, we talked to experts all across Siemens about a wide range of AI topics and how they are applied to different technologies. Spencer: Hello and welcome to the AI Spectrum podcast. In a recent podcast, I spoke to an expert from Siemens EDA about how machine learning is a vital component in the IC verification process, check out a transcript of our conversation below or click here to listen to the full episode
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