Last week, ASML got three of its biggest customers to publicly commit to the same roadmap item within 48 hours. TSMC was on Sunday, Samsung and Intel on Monday. All three signed on to move high NA EUV from today's 6-inch photo masks to a new 6x12 inch large format mask standard, and each company attached its own high NA milestone. Intel is already in volume production today with select layers. Samsung's targeting DRAM in 2028, and TSMC is targeting high volume manufacturing in 2030. And this matters a lot for ASML. Half-size exposure field was the biggest technical argument against high NA adoption, as we'll talk about in this episode. And so the high NA ramp is what ASML's 2030 financial model depends on, and this week made that ramp more likely. And this matters for the entire ecosystem because the entire mask supply chain now has a public, coordinated commitment to invest. They know that their customers are going to buy it. So that's going to be today's episode. We're going to cover what ASML announced, and more importantly, on why it matters for ASML and for the supply chain.
Hello everyone and welcome to another Semi Doped episode. I'm Austin Lyons. I publish Chipstrat, which is strategy analysis across semiconductors, AI, and data center infrastructure. Institutional investors read it, and so do technologists and executives from the biggest companies to startup CEOs. So check it out at chipstrat.com.
Hey, I'm Vik Sekar. I run Semiexponent, a research and consulting firm focused on semiconductors and AI infrastructure. I also write Vik's newsletter on Substack, where I cover everything from optical interconnects, networking, memory, packaging, power semiconductors, for a whole range of investors of various kinds and engineers. You can find it at viknewsletter.com.
All right, Vik. So, hey, what's going on over there, man? I feel like I haven't seen you in a while.
Yeah. It's been a while, actually. I think we needed a little summer break off. We had a week off at the end of August, and we were like, "Okay, let's just recover and regroup in September because, you know, stuff's always going on in this world, and if you don't take a break, it's not going to work out." So we had a little week off. But in that week, of course, we weren't quiet or anything. We were pretty much writing our Substacks and, you know, spending some time with the family. But what ended up happening was like in this week there have been at least like two or three new products that are all like personal AI agents that I've been messing around with a lot. The first one I messed around with was like Grokbot, and I was posting on X that I didn't really like it because it sucked a whole lot of tokens. But then, you know, the whole idea was that it has this little like CPU, a little like VM machine computer, right? And then it goes and like operates your computer screen for you and, you know, handles your login. We made a whole episode on Grokbot and its implications on like CPUs for AI. But then later, like, you know, several other things have come up. Of course, the most notable one being the Astra 6 from OpenAI, ChatGPT 6 Astra, which is actually kind of amazing. So I signed, I've been a cloud user for the longest time. So I was like, okay, here you go, here, you know, OpenAI, take my 100 bucks, and I was like, let's try this thing. And I wanted to try the computer use thing because it's like pretty fascinating. You can like, it handles your browser and all that. It's awesome. So I just put in like, "Hey, I need to book travel tickets to, you know, OCP Global Summit in October in the Bay Area where I'm going." And I was like, "Find me tickets." And so on the way back, I want to go to Japan where I'm planning to have my family over, and then, you know, I want to have a week there, and then I want to come back, you know, and all that. So it planned all this stuff for me, and it was like, "Oh, here's your ticket fare." And then it's like, "Hey, do you want to fly instead of Tokyo? You want to try Osaka instead so that your travel itinerary could be from Tokyo to Osaka? You could do your vacation, fly out of Osaka, right?" Yeah, yeah, yeah. Look up that ticket. So it went and like I could see the mouse moving, right? Like so it's looking at the thing, clicking buttons and typing in the airport codes, and I'm like, this is cool.
That's awesome. And you know, I like the magic of, hey, you told me to do this, but have you thought about that? I think that's where it's killer.
It went one step further too. It's like they tried the Osaka thing, and it's like, no, it's too expensive. Like your original flight was better in and out of Tokyo, but you'll have to backtrack your holiday back. Okay. Then it's like, hey, do you want me to try like booking only till Tokyo, and I'll book a separate flight ticket from Osaka to, you know, wherever you have to be? And I was like, oh, cool, let's, yeah, that's a good idea, do that, you know. So it tried out all these scenarios for me. It was awesome. So I'm going to play more with it. You know, what's a hundred bucks if it can help me plan a better ticket? Like I'll recover my $100 subscription in like no time. OpenAI should be sponsoring us. Like this is a pitch, such a great pitch. Yes, yes, OpenAI, if you're listening.
Um, I use both Claude and Codex. So my son, who's been working on his own indie game all summer, and he's working on getting it in the app store, and it's really great. He has Codex do all of it for him, which is awesome because then he can focus on the storytelling and the story itself, and it does all the programming. And then of course I use Claude and do a lot of Claude coding stuff. But I love your use case of like try all these permutations of travel to save money because I tried to do that. I know there might be better things out there, but it takes so freaking long that I'm just like, screw it, whatever. I'll just book whatever, you know. It's like I just get fed up.
And one more thing I haven't even gotten down to is like telling it like, hey, I have points on this airline. I want you to select this or that. You know, prioritize this or that. So it can be like, oh, wait, if you select this, you will get points on this airline on this.
So much is possible, and it's all like AI-based. It's amazing, actually. So I really find a use for this stuff. And I did another crazy thing, which I shouldn't admit on camera, but here goes, right? Because you YOLO AI all the time. So there is this other tool called Instinct, which I sent you.
It is like this little tool that has no information. So if you go to instinct.co, you see a web page that's like a letter written to somebody like, hey, you know, we're building this. No information about the company or what it is, or literally no pricing even, and it's only an invite-based thing. And so I happened to chance upon an invite, which I sent to you. So thank you to whoever's listening to this who gave me that invite. And so what I did was like, it's so dangerous. Do not try this at home, okay? Do not try this at home. So what I did was like I gave it access to my stuff and email and all that because I'm like, I'm curious. Like people are saying this is amazing. This tool is amazing. So if it's not really useful to me, I want to know. But for it to be useful to me, I need to give it some access. So I looked up beforehand, can I revoke access if I give it? And I looked, yes, you can revoke access. I'm like, okay, fine, I'll revoke it if it's bad, right? So what I did is I gave this tool all this, you know, details to my email and all that, and it actually keeps a track. And the way you use it is like you use it with your phone, so you can text it on iMessage or like WhatsApp. And so it sent me a message saying, hey, you just got an email. By the way, you've got your press pass for the OCP Global Summit has been approved. Here's the code you can use to get access. And I'm like, oh, cool, this is cool. I should do it sometime. But then I was like, I was working on something else, and then I was like, hey, you're supposed to be a personal assistant, right? I'm like, okay, go ahead and sign me up with the code. See you. Nice. I'm like, do it for me. You take care of it.
And then it was like, it went off. I went back to doing my work. After some time, it sent me another message, and it's like, here you go. You know, I registered you. I put your name down. This is your affiliation. I put down, you know, I put your meal preference as, you know, no meal preferences. And by the way, I ordered you a medium-size T-shirt. How did you get my size right? Or did you just guess it? Or did you know I'm like Indian, average Indian size? They're like, oh, did you guess the medium shirt? By the way, I ordered you a shirt. Like, what the heck?
That's amazing. That's pretty good. That's pretty good. So, did you revoke access?
Yes, I did. I saw my email come through with the QR code, too. Like, and then I'm like, oh, here's the QR code. I just click star on my Google Drive. I'm like, I'm good to go now.
But then I'm like, holy, this is amazing, but it's also too amazing for me right now. So I'm like, okay, that's it. I've revoked all access to it. I deleted it. They allow you to delete your particulars. There's a way you can delete on both sides. You can revoke access on the Google side, and you can delete any hashed information that is stored on Instinct. They allow you to do that. So I cleaned everything up, but it's pretty good. And then I find out that Muse, you know, Meta's Muse Spark is like another personal AI agent that's come up, you know. So I installed that, but I haven't tried it out yet. But I'm trying all these personal agents, and I think it can be really useful, to be honest.
Yeah, totally. Okay. Lots of thoughts. So when you sent me Instinct, you sent me just like a very short text. It was late at night here, and it was just like, hey, check this out. And it's a link to a website. I click on it, it's like instinct.co. And then it comes up. It's white background with black text, just raw HTML. Looks like the very first time I made a website. It looked just like that, you know. No CSS or anything. I scroll around. All it says in the bottom is like San Francisco, California. It doesn't say anything about who it is, whatever. And I'm like, oh, Vic got hacked. Vic totally got this. This is a .co website. He said something very generic like just check this out. It's got nothing here other than like sign up, you know? And I'm like, this could be someone in North Korea. This could be someone random, you know? Who knows? And so then I'm like, well, I'm just going to click and go a little further, you know? So I click to sign up, and then it's like, you know, will you give us access to your Google Drive? And it was like to write stuff, to read stuff, and to delete stuff. And that's where I stopped. I was just like, never mind. I'm just going to let Vic do this. And if it's not a scam, then let one of us YOLO it here.
Yeah, exactly. So thank you for YOLOing it. And, you know, maybe now I'll try. And I think, you know, zooming out, and I'm very excited about Muse. I've heard very good things. My son this morning, we saw it in the Wall Street Journal, and he was like, Dad, you got to try it. And I was like, I know. I know. I just, it's, I can't keep up, dude. This stuff's happening every day, but I want to try it. And I think what this is, is this is the evolution of OpenClaw, which was like the very early adopter, tinkerer, hacker, like I want agents. I want them to do useful things. I want them to have memory, but you have to like do a lot of work. And now this is the abstraction to like more of a product experience where more people can just go in and they can just use it and they don't have to think about all that stuff under the hood. So obviously like growing up from, you know, homebrew computers to like this is a Dell computer that you order and it just shows up for you. So I think obviously as we've said before, this does show even if it's not Grokbot, even if it's not Instinct, Muse, I mean, think about the reach of Muse. Like Meta basically has Instagram, they've got Meta, they got the whole family of apps. And so WhatsApp is theirs too, by the way. WhatsApp. So literally we could talk about hundreds of millions of people trying this, using hundreds of millions of cores on server CPUs that those people weren't using yesterday, and now they'll be using you in the next handful of months. So obviously there's huge implications for server CPUs, for memory, and so on. I think just this product experience of agents is worth tracking.
It's genuinely useful. If you tell, you know, my mom, hey, hey, you just texted stuff, it'll do its job. That is like the lowest barrier to entry for agentic AI. Like literally everybody can text. Like my mom texts me, you know, like if she can do that, everybody can use AI if it's through a text message. Everybody's on WhatsApp, too, you know, like it's amazing. I think it's come to a level of accessibility and usefulness. Actually, Grokbot was not, in my opinion, as amazing as what I saw ChatGPT 6 do with its computer control. It was actually pretty amazing. I think we are only starting the era of a new era of like really accessible AI. Right so far, people didn't really know what to ask, and in a text box, I tell my family, why don't you use Claude to do this? But like, what do I do? Oh, go to chatg.com or whatever. And then what? Oh, there's a text box. Okay, now what do I ask it? You know, so many problems. Now it's getting to a point of usability that is very much for the common person, not the geek.
Totally. I just thought of a use case for my wife to try it, which is we're trying to plan a family vacation with like my in-laws and with, you know, siblings. And so there's going to be several families, and, you know, she raised her hand and she's like, oh, this is going to be for my parents' anniversary, so let me schedule it. This will be fun. And then they won't have to worry about it. And once you start getting into that, you realize like, oh, everyone has calendars. Everyone has preferences. Every time you throw out like, what about this location or this location? Someone else like, oh, how about that location? And it's something different, and it's like a never-ending thing, and you're never going to make anyone happy. What if you could just have Muse or Grokbot or Instinct or someone do it? They can manage the calendars. They can throw out the ideas. And plus, obviously, it's nothing personal. Like I, you know, I think it's super annoying when someone comes back. They're like, okay, I see that you put time in here, but here's 10 other ideas, you know. But the infinitely patient AI can do it. And these are like real problems that I think real people would latch on to and be like, yes, please help me here. I will entrust some of my information to you and even pay for it so that I don't have to deal with it.
Yeah. And you know what we need to make all of this happen? Chips. And you know who makes them? TSMC.
So that's the segue to the actual part of our episode today. I love it. I love it. We should talk about... And who does TSMC rely on? ASML. So let's talk ASML. They had news this week. News with TSMC, news with Samsung, and news with Intel. So we're going to talk all about it in the next 30 minutes or so. So first, you know, I'm going to zoom back out. And I think everyone knows ASML by now, but for anyone who's brand new to the space, obviously ASML is the lithography company. And lithography is that step where you project the chip pattern onto the wafer, which allows you to etch or deposit or ultimately create the physical implementation of these transistors. And why ASML matters is they're the only company on Earth that sells EUV lithography machines, extreme ultraviolet. We have a whole podcast that we've talked about before. We'll link it to the show notes about lithography. You should check it out. There are some competitors in the space. Nikon and Canon still sell older DUV tools, but at the leading edge, it's ASML or nothing. And so what does that business look like right now for ASML? Just in Q2 2026, they reported in July, ASML's total net sales were 9 billion euros, roughly 10 billion or so dollars, gross margin of 54%, which is pretty good when at the end of the day they're making a manufactured heavy equipment machine, right? So to have gross margins of 54%, net income of around 3 billion euros, so 31% net margin on the quarter. This is pretty crazy. Again, I think, you know, in my head I go, oh, well, Nvidia makes, you know, 70% margins and these memory makers have 80% margins, so that's like a lot less. But when you actually think about ASML as making equipment for manufacturing, like these margins are crazy. Which of course they have earned because it's probably the most complex machinery in the world. So the question is, before we get into the news, one of the things you might have noticed in the news that I talked about, a memory company, Samsung, and logic companies, TSMC and Intel Foundry. And so one thing that's worth noting is actually ASML makes their money by selling systems and by servicing these systems. And actually, the servicing of the systems was 2.8 billion euros of that 9.3 billion euros. So almost 30% of their revenue is actually recurring revenue. It's like subscription revenue. There's kind of an analogy here. Like if you think about Apple, obviously they sell devices, but then they also want to sell, you know, software and services and get some recurring revenue. So they're not just having this lumpy like we sell devices during the holiday season kind of thing. ASML is trying to build a similar business profile where of course they sell these $400 million machines, but then they also have this ongoing revenue of helping to maintain those machines. But of those system sales, actually 51% go to logic companies and 49% are for memory companies, at least back in this quarter. That actually was pretty surprising to me. I kind of just, you tend to think more about the bleeding edge EUV for logic and not as much for memory. But was that news to you?
Yeah, to me it is because I'm always a little behind on catching up on the lithography and like wafer fab equipment space. But what I wanted to mention is when for people who are listening to this, when you say logic and memory, fundamentally they are different process technologies, right? So when you say 2 nanometer transistor or 3 nanometer transistor, by now we've explained before that this is by this point a way of saying it. The dimensions aren't actually 2 and 3 nanometer. It's like a gate-all-around where they stack multiple gates. It's a different structure. But the nomenclature has stuck, right? So that is like one side. That is what you refer to as logic, right? And memory technologies are actually quite fundamentally different because the transistors there, even the leading transistors there, are more a 10 nanometer class node. So when you say exactly like EUV is required for memory technologies, it's not to make the transistor, is it?
Right. Right. It's really about memory is all about getting as many bits as densely packed as possible so you can get as high of capacity as possible and really try to drive down the cost per bit. And part of that is making capacitors and making the transistor and capacitor structures as close as you can. And so a lot of it is about, yeah, the sort of pitch, the lowest pitch that you can get to try to really kind of cram everything in.
Right. So the memory nodes have basically this capacitor and a transistor. So what they call this is like a one transistor one capacitor or 1T1C technology where, you know, the capacitor holds the charge. If it is holding charge, it's like a one. And if it's not holding charge, it's a zero. And, you know, the transistor then turns on or off to charge it or recharge it or discharge it, right? So that's how memory works. So creating this 1T1C structure is where EUV lithography plays a role in memory. And it's amazing that it's like nearly half the utility is nearly half. So EUV shouldn't always, you know, be thought of as getting to the next greatest transistor node because wherever you read it in the news, you will hear it as, hey, this is for A1 or A7 nodes, which is like the angstrom era nodes, which is basically when you say A7, it's basically a 7 nanometer transistor equivalent, right? So that is what you usually see in the news as to that's why we need EUV. But you actually need EUV to make memory as well. So that's the only thing I wanted to like clear up just to make sure that people understand the difference between these two regions.
Yeah. Yeah, that's helpful. And so to that point, when you look at the geographies that ASML sold to in this most recent quarter, 43% of their system sales went to South Korea, 30% to Taiwan, 14% to China, and 9% to US. So again, you can think, oh, South Korea, memory companies, Taiwan, you know, obviously logic. China is an interesting one because at first I was like, what? They can still sell to China. But of course, we're talking about older DUV equipment that you can still sell to China. You know, for people who haven't followed as closely, the ASML story was, if you go look at how much they sold to China, there was a period within the last few years where it really spiked almost up to like 50% of their sales, and then now it has come back down. And that was obviously China seeing the export controls coming and so buying up as much equipment as they could before the export controls came. And then United States, you know, obviously there are also obviously Intel Foundry is buying leading edge stuff, but there's lots of other companies that are buying older tools. You know, you can think of the GlobalFoundries of the world. So, you know, United States is small, but it's not, and that's not just Intel Foundry. I wonder if it's Micron for memory.
Oh, yes. Yeah, true, true. Definitely. Okay, so let's move forward. So why was there so much news this week? Well, this week all of these announcements were clearly timed and coordinated around the conference SPIE Photomask Technology and EUV Lithography Conference, which was in Monterey, California. Nice place. I was like, dude, we should go next year. Monterey is cool, and we could learn a lot here. And so SPIE is the Optics and Photonics Professional Society, and this particular event is the annual gathering of the photomask world. So this is like the people who make and use masks, whether it's captive mask shops inside of Intel, TSMC, Samsung, merchant shops, names like Photronics, DNP, Toppan, which, you know, I don't know a ton about these companies, and so it'll be fun to dive into them more. You've got mask blank suppliers like Hoya, AGC, mask writer companies, inspection folks, because not only do you have to make the mask, you have to pattern the mask, like draw your particular layouts, but then you have to inspect it to make sure that it is correct. And so you've got companies like Lasertec, KLA, Zeiss, all in this space. And so they all come to this conference, and they are all in the supply chain of making the photomask. So I guess before we talk about what changed, would you like to introduce the idea of like what is a photomask? What are some of the dimensions, kind of like what's the current state of affairs?
Yeah, I'll tell you what I know, and you correct me if I'm wrong, because this is how I learn on the podcast live. This is good. So photomasks are essentially patterns that are on a different substrate-like thing other than the wafer itself, where there are like patterns where the transistors should be or should not be. So you can have like a positive photomask or a negative photomask, which means the patterns on the photomask can selectively block light from hitting the wafer underneath, or they can selectively let light through. So however you want to look at it.
It positive or negative, and then depending on the materials used, you know, parts that are exposed or not exposed remain, and the rest can be like etched away, right? So this is how basically features on the wafer are formed. And fundamentally, wafers have been 300 mm on camos for a long time; those are 12-inch wafers. But the masks that pattern on these 12-inch wafers have never been 12 in. They've been 6 in. I think they've been like 6x6 squares or something like that.
Then what happens is now if you have to pattern a 12-inch wafer, you have to use the 6x6 in like different spots on the bigger diameter wafer, and you have to kind of stitch them up together eventually. That takes time, and it's like complicated to do. I think the benefit that you're going to tell me next, and we'll hear more about this, is that now we have gone from a 6-inch lithography mask to a 12-inch lithography mask. So we can pattern the whole thing in like one go.
Yes. Yes. So high level, you're right, and we'll get into the details. The mask is all about how you end up drawing where the transistors go. And the mask, the standard mask, has always been a 6-inch square, 152 mm x 152 mm. And it is basically like a quartz plate. And ultimately, so the question is, you know, how do you draw these tiny lines to allow the EUV light to either shine through or get blocked? And that's traditionally done with a tool called an electron beam, like an e-beam writer. And it's a very slow process to draw this, but ultimately you write on the mask, and then like Vic said, this ends up getting used in the semiconductor manufacturing process. Today it's 6x6 in, and that works fine for low NA EUV, which is what the industry is using today. Let me see if I have some notes on low NA EUV. So what happens is you have this 6x6 in mask, but with low NA EUV there is this sort of demagnification step where it gets shrunk down by 4x in both directions. So the final actual reticle size that you're patterning is 26 mm by 33 mm. And this gets you that 858 mm squared for the full reticle size, the kind of like postage stamp. And if you remember in the Hopper era, it was like, oh yeah, every Hopper is reticle sized. We cap it to that. And the reason is because you want to just make the mask, one mask, and then pattern it all over the wafer and get as many like Hoppers as you could. And then if you remember in the Grace Blackwell era, they actually said, 'Well, we need even more transistors and more compute. So we're going to, we can't make that reticle size any bit bigger because there's this whole industry around the photo masks.' And if you know, so let's just take two, let's create the Blackwell GPU, but we'll end up stitching two of them together to kind of create a GPU with two dies. So that's, yeah, that's the way that the industry has worked so far. Now...
I want to just step back for one minute because I think there's an analogy here that will help people understand this if that was a little difficult to follow. The reason I'm saying this is that lithography requires a certain amount of expertise to see how it actually works. But let me see if I can like throw a little light on it. So when you're saying low NA, I think we should just mention NA stands here for numerical aperture. The larger the aperture, the better. The higher the numerical aperture, the better. Better in the sense that you can make smaller transistor dimensions. That's the one thing. So we are now going from low NA to high NA, which means we can make smaller devices and patterns. Okay. The other thing is this reduction in size that you mentioned, like 4x, right? The best way to think about this is think about a telescope, like the ones that you know you can like look, like the mariners use at sea, right? Like imagine Pirates of the Caribbean like looking out through the telescope. Now imagine that you flip it upside down, like you look through the wrong end, and now everything you see is smaller, right? So that's the same idea here. So you make a mask that's 6 in x 6 in. But then you're like, wait, I drew these large patterns on a 6 in x 6 in. How can I pattern a transistor on this? So the whole idea was, this is actually a fun story that we wrote on this section on our daily Semi-Doped newsletter as to how this whole thing came about. So if you like the history behind semiconductors, we write a lot of it on the daily Semi-Doped, especially on like Saturdays. So you should check that out. A lot of good stories for your Saturday morning. But you know, when you flip the microscope around, everything looks reduced. So you can use a bigger mask to imprint something like much smaller on a wafer because the light reduction is very important. This is a very important feature. This is called projection optics. And projection optics only allows us to make patterns that are larger than the transistor and then reduce them to make like really small transistors. Otherwise, we can't make them. If you have to make the mask as small as the transistors themselves, how are you going to make that? Right.
Yeah. So projection optics solves exactly: make it big and shrink it down using light and light tricks.
Yes. So that's why we make these masks. The thing is that it still requires several steps to do this because it's not as big as the wafer, right? And so you can reduce it to make a Hopper era GPU that is like one reticle size. But then people were like, that's not enough. I want my GPUs to do more. But you know, there are only two ways you can get more compute on a chip: either you make the transistors smaller, or you make the chip bigger. So they're like, okay, we can't do the reducing the transistors yet. So we'll just stitch together two GPUs to act as one, like the Blackwell era GPU, and now you've got a bigger 2x reticle GPU that they kind of stitch together by shooting two of these pictures from the mask together.
Yeah, exactly. And by the way, as we talked about advanced packaging, that's how you stitch together these GPUs. So obviously advanced packaging, CoWoS, EIB, very important. Okay, so back to when you talked about low NA and how we want to move to high NA. So the numerical aperture, so the question is like, how do you make smaller transistors? And the simplest way is to decrease the wavelength of the light you're using, shrink the wavelength of the light you're using. But EUV lithography uses 13.5 nanometer light, and that's very specific, as we've talked about before. It has to do with tin and these droplets, and you're hitting them twice with the laser to essentially pulverize them, and they give off light at this certain 13.5 wavelength. It's not so simple to just be like, 'Oh, well, find a different material that gives off 9 nanometer wavelength light.' Like that's not so easy. So the other dial that you can turn is, okay, we can't change the wavelength of the light, but we can change the numerical aperture. And so if you have a bigger numerical aperture, you can have finer resolution. And so you can ultimately resolve smaller transistors. But there's a big trade-off. When you go to a higher resolution, in this case going from low NA, which is 0.33 NA, to high NA 0.55, what happens is you have to use something called anamorphic optics, where this is kind of like widescreen film, and it's like a trick that they use to make that work. But ultimately, a wider aperture means that the light hits the mask over a wider range of angles, and the EUV mask, they're actually mirrors. It's like this 40-layer molybdenum and something else reflective mirror. And...
Yeah, and so that multi-layer mirror only reflects well within a narrow cone of angles. And so at 0.5 NA with the 4x projection optics, the steepest rays coming in would fall outside of that cone, and so it didn't reflect as well. So what ASML does is they make the demagnification step anamorphic, where ultimately what this means is they keep 4x in one direction, but they actually go to 8x with their projection optics in the other projection. And so this works, but ultimately what happens is the area that it projects on ends up being half as big. Instead of your final sort of reticle size projection, your exposure being 26 mm by 33 mm, you ultimately get 26 mm by 16.5 mm.
So that means with one exposure, you get half of the area ultimately.
Okay. Okay. I get it. So I'm learning this anamorphic optics, right? Okay. Like the term aside, I think the basic idea is that instead of the 4x reduction on x and y, you have 4x on one side and 8x on the other side, which means that, you know, you have now Honey, I Shrunk the Kids. I don't know if you know that movie from like a long time...
You're old enough to know that movie, yeah. So you shrunk the kids too much on one direction, which means your transistors, your feature sizes get like much smaller along the 8x direction compared to the 4x direction. That automatically means that your size of the reticle has dropped by half in that direction where it was 8x, where you shrunk more, the dimension drops by half. Right. So that's what this whole thing is.
Remind me what it is to do with the mirrors again.
Oh, just the way that as you increase the numerical aperture, as the light comes in at like a steeper angle, it doesn't travel through the mirror sandwich as well.
