Reflecting on Smartphone Photography Evolution

Welcome , yes, welcome. This is phone and show chat 895 and no your ears are not deceiving you. This is Steve Lichfield, back from semi-retirement by kind permission of Ted and Joe. I had an uh chat with an old f friend and favourite of the PSC podcast, Yuha Alaka hu, the uh guy from Nokia behind the Nokia 808, uh Lumia 1020, etc. Um he's now consulting for a firm called I O EYEO who have a revolutionary and a genuinely impressive uh new way of creating camera sensors that capture seventy percent more light in the f potentially in future smartphones. We're looking a couple of years in the future, but it's a great glimpse into what might be. So uh if you're not interested in camera imaging and phone imaging, then perhaps skip this show but we have about thirty five minutes or so of imaging related chat uh as pertaining to smartphones of the near future and it's I think it's pretty interesting. We go through an um an introduction to the main guide behind the company, and then we'll slap back into me trying to get my head around exactly what's going on and how this new nanophotonics technology works. So grab a cup of coffee, brace yourself and here we go . So I'm here with uh two guys um Yuha Alakhahu who I'm getting better and better at that pronunciation I think ten twenty and I think probably the nine twenty and the eight to eight before that, all of those devices. Um so hi Hugher. How are you doing? Hi Steve. Good to be here. Long time not Steve. With you we have your I think your your b your current boss. Um is Yeroen, I don't know your surname. Yeah, it's a very Dutch name, unfortunately. Okay, maybe you Hawk can send the the the spelling of uh and your name through through the back channels. Um tell tell us a bit about yourself and uh your company is called AEO. Is it is it IEO AEO? IO. So uh from eye opening because uh essentially we let the sensor see more so it's eye opening IO. That's where it's coming from. Yeah. So it's an English joke. It's English gimmick. Yeah. It's it's spelled E Y E O for people listening to this. Um tell us about how your your your background over the last sort of ten, fifteen years and how you got to create the company and the idea. Tell us about that. Yeah. All right. Um so nice to meet you first of all. Uh happy to be here. Um yeah so my background is is yeah obviously in imaging and in uh chip design. So I spent uh quite a few years designing chips myself, uh, but then later moved more into product management roles and then business line management. Um so initially I started in a startup company called IC Sense where I did chip design. Later I went to KLA, uh a fairly bigger company uh where I was running a uh business uh line of products using cameras. So then I was more a user of uh of sensors. Uh and then after that I joined uh Kyleste company, which is um specialized in developing custom image sensors, uh, and did that up to about uh a little bit more than two years ago, two years and a half ago, uh when I was in contact with IMEC, uh when I met Jan Genu, Professor Jan Genu, who is the inventor of yeah, the technology of color splitting that we're using today. Um and for me it was yeah an eye opener. Um for me it was immediately the the the solution to a key problem that this industry industry has been facing. So decided to leave uh my position at that time um and I spent about one year uh at IMEC essentially to establish the company, to uh assemble a team, find investors, uh find suppliers, partners to work with, and even first customers, uh, essentially then to uh leading to end of twenty twenty-four when we started the company uh together with co-found ers, um, and then yeah, raised first money uh exactly one year ago, fifteen million, uh and that's then to develop a first prototype and a first product, which is well on track. Uh and then uh actually two days ago we announced another forty million investment series A round, uh which we closed, which is essentially even to speed up because yeah we see a lot of interest from the market and yeah we wanna capture that uh momentum. Yeah, yeah, yeah. So your company IO um d just to give people a an overview before you go into detail, it's a new sensor technology and which essentially gathers up to seventy percent more light. Is that is that a fair summary? Can you explain this? On your website it talks about a nano photo photonic technology. So I as I understand it, and I'll go back to the Lumet and twenty here. In fact, the eight oh eight before that these these UHA, if I understand it right, they use um true RGB sensors. There's no quad bay or anything. So ever the light hits a grid of red, green and blue filters literally at the pixel level and then software in the two Lima ten twenties case or hardware in the Nokia eight oh eight's case famously then oversample oversamples and and combines these colours into what it thinks is the true image. In modern phones in the last ten years or so we've had quad quad bea filters so rather than being as sophisticated as two ten twenty you have a grids of four four greens, four blues, four reds, etc. Um and so the the the software is gathering in colour information in blocks of four, and then it uses algorithms to try and work out what the colours are of the individual pixels. There's a lot of wastage going on, a lot of wastage and lots of computation. And I'm assuming this is kind of the solution that you're trying to fix with this technology here. And so how does how does your nanophotonic thing fit with these um comp arison to these quad bear filters and how on earth have you managed to make it so how how has the invention managed to make it so small and what and tell us how it works? Yeah. Uh yeah indeed it's a good question. So um I think we so we're solving two aspects uh so the two that you mentioned the first one is the basic filter itself uh because as you mentioned you have a green pixel a red pixel blue pixel and de determined by the filter on top. And so the the problem is essentially that if the light hits the for example the green filter, then only the green portion of the light will go through into the CMOS and the red and the blue is essentially blocked. So you throw away two parts of the spectrum, two thirds of the spectrum is gone. Um so that's the first thing we solve. So we are not filtering any light to see color, but we are splitting the color in the different color components and then we inject them into separate pixels. So the result is now we didn't throw away anything and so it's not even just seventy percent better, it's times three better. Um so it's really a huge step forward in the amount of light that is collected inside of the pixel because of this. And then the second thing we're solving is the essentially today they use indeed WildBayer and you have even Nona Cell and Tetracell if you combine four by four pixels like this. The reason why that is being done is because if the smaller you go in a pixel size, and definitely if you go below one micron in pixel size, the effect is that let's say your photon is actually bigger than the than the than the square of the pixel where you want to get it into. And so if you would not do any binning, any combination of pixels, then you would lose even more light because the the the photon would essentially only a parcel be on the red, but actually the majority will still be captured by green and blue and be filtered away. So the loss if you go smaller in pixel exponentially increases. Um so the second property of the technology, and that's where it makes it also very appealing for smartphone, is that in the structure we 're not only splitting the lighting color, but we're also compressing the photon into a smaller size. And so now we end up with a photon that is, let's say, in different colors, guided into different pixels, but also at a size that actually fits a zero point five micron pixel and so now you don't need to bin to to bin it anymore in quad buyer or what you want to call it um but still get maximum performance so you didn't have any loss uh associated to that. Um and that then also relaxes a bit the the the computational needs because if you combine these four by four and uh pixels, yeah you have a bigger interpolation to do on pixel level which which complicates again the software part. So let's say it it fundamentally from the start, from the input, resolves the filtering and also the need for this binning. So how large are the I mean these these first of all, how large are the pixel splitters uh sitting above the sensor? How large are they compared to a traditional pixel, traditional quad Bayo block of four? How many how many sub pixels are your nano um splitters covering? Just so I understand it. Yeah, so today you have um so one splitter splits the colour into uh the light into two parts of the spectrum, so two colors essentially. Uh and so we inject it into two individual pixels below. So you have to imagine one structure sits on top of two pixels. And then we have two versions of the structure. So we create again four colors. So two times uh one color and then the other one is uh set of other colors so at the end we st we again have four colors but uh yeah let's say without any loss um of light. Okay, so the the the light hits the the top splitter, which splits it into two, and then each of those two goes into another splitter, so you end up with four buckets at the bottom. No, so it's a bit easier than that. So it's really one splitter at the top. So you so the photon hits the top of the splitter, it's then guided down split into two parts and then injected into two pixels. And and that's the end of that chain. Uh and then we have a a second splitter just next to it, which is designed differently, which again collects one photon, splits it into two other colours, and inject it again into two pixels. You four uh different like color au put. So you effectively get like four different singles What are the four different colours in terms of frequencies that you're So you let's say you can uh compare it to blue yellow is one pair and the other one is cyan red Okay, so this is very a very different um system in terms of what the algorithms will be expecting when they when they read your senses. be read in terms of uh in putting them into consumer products. Will it w what will it need at the the hardware end? Yeah. Yeah so indeed and you will have to do some different uh processing of color of course because yeah, we don't start from the RGGB uh like the industry is uh known to. Um on the other hand, yeah, we we already have an uh processing pipeline up and running where we only had to change a minor amount of building blocks. And so we can actually do to simplify it, a color conversion in the beginning, um which then converts it into kind of red, green, blue again, and then you continue with the rest of the pipeline. So you can go into quickly into a compatible mode, I would say. And if you do it in this way, you immediately have the three times more signal. So that you always get uh and then you have the color performance of the RGB uh color quality I would say. Um the reason why we chose these colors, so why we chose these four colors is because it's actually matching more the human eye response. So effectively we are already closer to what you want to represent in your picture. And if you look at an ISP pipeline, it starts from RGB, and then you go to a YU V space. Let's say the color space where we start from is already closer to that YUV space. Um so over time , uh our customers they can also start implementing new algorithms that skip parts of that RGB flow and then also get a better color performance out of the chain. What resolution sensors are you envisaging for this? Have you actually made any sensors and if so what resolution? Yeah, so we're currently uh the first uh evaluation kits that they're they're currently running through the foundry as we speak, um and they will be delivered to first customers uh after the summertime. That's more of a demonstration unit. It's a small uh one megapixel imager, but that's more to demonstrate the difference, the comparison between the filter and the splitter. But it's already using a fairly small pixel, SO consumer grade pixel. And then later in the year, we will be releasing higher resolution variants going up to 64 megapixel um of a similar pixel and a similar technology. So if I've understood it right, for a sixty-four megapixel sensor, you would be using sixteen million um nano nanophotonic splitters. Is that right? Uh thirty-two million years. Because of the two, two to one. Okay. How are these tiny, tiny little um I presume that are they are they are they gla then they can't be glass, surely. How how are the how are the nano photonic um splitters made? What are they made of? How do you express them? Yeah, so it's a combination of silicon nitrite and silicon oxides essentially what um is also being used in in in silicon photonics platforms. Um so you can even find that back in uh transceiver laser receivers uh that they use in data centers for example. So uh it's a fairly kno wn uh C MOS material. Um and with that we make indeed a kind of uh like small um yeah uh we call it waveguide. So it's essentially a waveguide, so a funnel and a wavegu ide , um in which then the photonic operation happens. Um and let's say the challenge is definitely, and that's of course one of the of the it's part of the knowledge we have and the experience we have built is how to create thirty-two million of them on such a small space aligned and linked to individual pixels. And so that's of course one of the key elements of the technology. It's something that was developed within IMAC over the last uh six, seven years now. So it's already well established how to do that. But that's indeed one of the key elements of the invention. Is it made at the s the the the photonic splitters and the the sensor itself, the the the photoreceptors, all of that is is created in one one one layer, one process then Yeah, so you need multiple steps to process this, of course, but you can imagine or you can uh see it a bit similar as how the CMOS chip itself is made or even how they make today the color filters and the micro lenses. So it's also a set of process steps additional on top of the uh of the wafer. Yeah if I'm asking uh questions in which I seem a bit to be floundering around is because uh every single person hearing this and in the future years we' sayll w what ? How on earth are they doing that? How does this work? There's going to be a lot of question marks I think. But um so we've covered how kind of how they're made and you will eventually get up to s to consumer um phone resolution because people these days they do expect twenty-four megapixels, I think as a minimum. That's what the iPhone standardized on standardizes on. So we're looking at that kind of resolution. How how long do you think it will take you to get to that resolution shipping to real consumer phones? Yeah. Yeah, so that's definitely that's definitely an interesting question. So let's say from the technology point of view to apply it on top of that kind of resolution, that's essentially what's already happening in the next several months. But then it's more of a for the next generation device. And that's also where we raise new money for is yeah we have to add more functionality again in in those imagers itself because the the the phones, the smartphones, yeah they have many, many features inside of the chip, inside of the sensor to enable them to exploit the whole toolbox of image quality at the end. Um and then of course it's also about sh uh demonstrating ramping up into volumes at high yield uh at the cost level that is required. So that will take steps for us as a company as well to establish that. So that's ongoing. But let's say in the next let's say by twenty twenty nine, twenty thirty uh time frame to be in a phone I think is a good target and a realistic target um for this kind of technology. Yeah. So we're talking if but people wouldn't see this in a consumer phone probably un til the end of next year Yeah well probably because there is a trajectory to go through let's say including also page and everything. So let's say I would say more ends twenty eight, twenty-nine time frame. Okay. Um Yeah. But they w it when people but they now talk about Quad Bear and they p p everyone knows what Quad Bear is. What's what's the official title for your system . Nanophotonics. No, so we we branded it the the yeah the NCOS nano colour splitting um technology. Um and cost technology. So um yeah, just to show that it is indeed splitting color instead of filtering color, which is really the fundamental difference. Okay. Juha, what's your role in all of this? What do you do? So I'm an advisor for for IO. But I've been now involved with you guys maybe um like a year or something like that? I think even a bit more than a year. Yeah, yeah, yeah. When I first talked to Yero en was like There aren't so many things you can currently do with image sensors. The quantum efficiency of the image sensors is already like 80% or something . Um noise floor is like one or two elektron. It seems very mature technology in terms of performance. Okei, when Jaruen was talking about this. This is the last mystery to solve in the imitators. I want to be involved in this. Image sensor technology. This is really like the last mystery to solve in the overall image sensor lowlight performance. I'm of course helping with overall uh like image processing and uh kind of uh how how could we best implement this in uh in products uh smartphones and and uh several other other products so yeah doing my best to make it make it happen. How expensive is this going to be in terms of but the the physical component? Say for a a phone manufacturer in twenty twenty eight that is implementing an N COS sensor in their camera the the amount of process steps, the amount of process layers that we need is reasonably similar to what you do today with the colour filter and microlens. So of course today the cost is higher because we are not yet at volume and not yet yield optimized as so currently the cost is higher um but over time we don't see a reason why we cannot get the cost down to similar levels even uh but yeah that that requires going into volumes uh and and and improving the the yield uh uh as you do for any phone. So that that's something we need to do anyway. But then there is a a big um um improvement anyway which is that because we have three times more light, because we shrink the photon and everything, yeah, we need less space. We don't have to bin anything to to get to these twenty-four megapixel effective resolutions. Um so it means that your actual sensor can become smaller again for the same performance. And then of course you win on cost because yeah, at the end of the day it's really the size of the chip which is uh driving the cost not only of the silicon, but the complete module and everything. So the cost aspect is something we can definitely get under control for smartphone. For our first markets, our first products that we're introducing, we' intreroducing them into more higher uh quality or higher more expensive camera markets, uh where the cost is not so challenging yet, um and where there is also a willingness to pay more for a sensor that basically offers a much better product at a higher price. So um also for the starting point we can justify a higher uh price because of the extra performance you get. Yeah, I was looking on your website and it talks about um in addition to consumer electronics you've got automotive so putting it into car cameras. I just used I bought a car two years ago and I'm I'm staggered by the number of cameras it has around the front and the things it's photographing and and and s the sensors it's it's triggering all the time. So there's lots of um imaging in cars. You've also got medical imaging, which I think is what you're talking about then when you're the price of a product, the camera is is insignificant compared to the rest of the medical hardware and security again where you're so all of these things of these four categories consumer electronics, autom otive, medical imaging and security, um you think that can you rank them from in terms of uh the your priorities and and the the the time and scale on which they'll come into the market for you with NCOS? Yeah, so the the first products would say we focus more on the surveillance smart city security sp The fact that we have three times more light, yeah, it essentially means that in darker environments uh the sensor will collect more info and with color. So this is an obvious market, and also the first products that we will bring into the market and that will happen in the next year. Um will be um targeted for this for this segment. But at the same time we do get um requests from automotive and medical and so on for I think also obvious reasons. If if in a car you can have uh again in lower light, better detection of objects uh with color in the street, it can make a difference for an ADA system to recognise a bag from a rock, for example, or or anything, and then make a different maneuver based on that. Um so we're also exploring if the sensors we already bring into the market uh can can also serve their needs if it fits the right resolution or anything. Yeah, yeah. I'm quite excited by your uh your con your contention, quite rightly, that but you can now get a camera sensor and thus the whole camera body and lens in in in the phone world can be a lot smaller for the same quality. Now that may sound like you're not actually gaining quality, but by making everything smaller you can eliminate in theory cat come of these camera bumps because even mid range camera phones now they have a camera bump that's two, three millimeters because of the literal size of the cube that houses the camera. But if you're gonna get this exact same quality uh with with NCOS technology and and the whole camera cube could be say half the size. You wouldn't need a camera bump. Phones would literally be slabs with with no protrusions and they wouldn't when you put them down on surfaces. Yeah, definitely definitely, yeah. And and that there is definitely a big driver for for that. And I think there is also another benefit you can imagine is, for example, the front-facing camera, where today you have a notch in the screen to house the camera itself. Um already for a while people are trying to put that camera behind the screen, but the screen itself is also filtering light. Yeah. So now we can then make a camera that doesn't filter any light with the um the the display in front, uh which then filters light, but the overall performance may still be the same, with the difference that now there is no notch anymore. Uh the camera is just behind the screen. So how come nobody's thought of this before? This sounds revolutionary. Well people have attempted to eliminate the color filters in the past as well. So there have been other attempts. Um so it's not an easy problem to solve. So I think the the the the fact that the the problem is there is very known. Um but the solution to it is far from an easy uh problem to solve. Where other methods have failed in the past is, for example, if they needed very exotic process steps which are not CMOS compatible, and then you end up in the uh the problem that if you want to ramp it in volume, it's difficult to to get it under control. So that's something we do differently, is that it's compatible with existing uh CMOS production flows but so we have seen attempts in the past fail yeah because they changed too many things to the process which which complicated it and the second part is yeah the um many of the other um attempts were maybe more ki kind of prism based, I would say. So so where you have um light coming in unless you know a prism, it scatters it in kind of a rainbow uh out to then um project it onto pixels um but then one of the issues is that uh you have a lot of color cross talk um and then you need a lot of computation and that company computation in itself introduced I'm I'm Juha, going back to the dawn of camera phones. Um I'm thinking Nokia seventy six fifty, Nokia N seventy, all those pr pr the very first cameras in smartphones, they still used microlenses and RGB sensors, didn't they? And I I know we're not talking about colour bin quad bail or binning or anything, but they they all use microlenses on their sensors. Yes, correct. So so the m microlenses have been uh obviously uh existing in centros as long as I can remember, and they all of those centros used Bayer, Basic Bayer R GGB. There have been some uh smartphones that have been trying some alternative color patterns, like replacing one of the greens with white. But the benefits of that haven't been significant enough to make it like a mainstream there's some other problems. For exempt, if you have one white pixel, then overexposed sooner than the other three. And that creates problem in the auto exposure. But with I.O. you don't have any of these, let's say the overexposure problems effectively all of these pixels are equally sensitive. Tonet. If you tell people, we by ch changanginging the the sensor technology to c sort of quad bear etc to to with microlenges to to at N costs, you can get seventy percent more light captured. It would blow people's minds. This is this is this is literally eye opening for me to to follow your real branding. This is this is amazing. Um or three X like I care inside. So so seven seventy percent versus three X like it's it's actually e even more than seventy percent . Well yeah, this is really impressive. So I I wanted to get this on on on the podcast so that the the the whole c community could could find out about have a heads up that this technology was coming coming down the line and I do wish you well with it. Just um t take us through just very, very briefly. What what in terms of your own personal technology, youho, what are you using as your main smartphone right now? Hey I have iPhone seventeen Pro here. Uh huh. With with this nice ARC uh bumpers. But but hey, uh I switched from iPhone 13 Pro to 17 . And they kind of uh you know, it's five generations, and if I put them side by side, the difference is is relatively incremental. So personally, we need some I feel like we need something like IO . I'm doing lots of true HDR output fotography. For exempt, Lightroom has really nice support for like true HDR tuning and going like wider than white and when you use it korrekty, I think that's like a like nice, nice, impressive thing that has been really making my own photos better recently. But yeah, I'm I'm an iPhone 17 Pro switch five generations and I don't know when what is my next one, maybe . I don't know. I want to end something. Well you've got to wait till uh N COS hits hits the iPhone. I guess I've been three presumably you'll be talking to Apple at some point, Jorin We can of course not disclose that. Yeah. What are you using as your main smartphone at the moment? Well, yeah, so I I hate a disappointment, but it's the same as also an iPhone seventeen Pro. Uh so I've also been an iPhone user for many, many years. Um I I use it uh a lot when I explain to people what the impact is because yeah one of my previous ones, I if you compare it several years ago, there was not even a bump and it was all much smaller in the camera side. So I would love to see it again smaller, but then outperforming today's um quality of the picture. Yeah. Of seven seventeen pro max. But I But I completely agree with you. I've got also got an i Phone 14 Pro Max and going back an iPhone 11 Pro and I can't tell the difference really for most light conditions. These huge sensors and these huge lenses and all hopefully to be reduced by end costs in years to come. But um that they they're only really to cover the edge cases where you're taking a photograph of your child in low light or trying to take video of a concert or something. And that's when these new latest phones and latest technologies and latest sensors that's when they kind of come into their own. But for most tu purposes, the f the cameras in most phones now at Uha I would say, you can buy a two hundred dollar, two hundred euro smartphone and in and ninety percent of light conditions it will take ninety percent the photographs that y that you need. You just don't need a flagship. Yeah. W one one other thing I would like to maybe highlight here. I I like one thing I don't really get in my karma , it's like how oversprocessed it still is, even when I'm using this ROA-output. So I've been using this light application. I don't know if you ever And that actually reveals the true kind of RAW RAW output. And I actually like the look of that quite a bit in my own photos. And then I can later on decide if that specific foda needs noise filtering and if like that's another thing i i hope that this new hardware innovation like IO can improve like we would need less that kind of processing uh to make and and still get good good high quality output. Yeah, I'm guessing Joran that um with that NCOS implemented in a few years' time, you th people will still have the choice the, c the the phone makers and the users will have still have the choice of producing a processed JPEG or a ProRAW or similar. That that won't change, just that they'll be getting higher quality input data. Yeah, absolutely, yeah. So at the end the the the the the smartphone manufacturer or the end user, anyone can still choose to apply every possible technique they do today for for uh picture modifications or adjustments so that that is always possible. But indeed we give a better input from the start. So it's like you you start at the at the at the starting point with a Formula One car instead of a I don't know a regular car um but then you can still do whatever you want with it afterwards. Um and it's also interesting because I I've been in another discussion few months ago where it was about exactly that comparison between the raw versus the completely processed image and uh especially with AI now these days you can even there were there were uh like messages where why why do you even need a camera? Because you can start asking AI, create me a picture of me and my family uh at Times Square and it will basically do that and the picture will look perfect. But the problem is the emotion and the authenticity is completely gone. So there is also now a trend to actually get more authenticity um and the emotion essentially back into the into the picture. And so if we feed it with the most raw pure data um at the start you can you can you can also leverage that and and and and I I think from a from a creating memories point of view with pictures, the fact that we offer a better input is is is is is really the key. And then you can still decide for yourself how AI or how processed you wanna make it at the end. Yeah, I still find you hard that it's some of my favorite photographs I've taken ever of people. Um were with this um uh and I I've got some shots in it taken in broad daylight, but using fill-in flash with the Zen and Flash to highlight to highlight shadows and people's faces. Amazing photographs, and this is a twenty thirteen phone.' Thats's what that thirteen years ago now. In the modern age I've been cutting cr um unprocessed shots. I've got the Sony Experia one Mark V here and and Sony's image processing is fairly gentle. And I think you you might also like that you har in terms of it. But I I had to say I actually I was uh browsing through my old photos and and I was thinking, hei, what was the SLR I was using in these because they look so nice? And then oh it was actually 808. And of course you know it was not amazing in in low light without the flask. So put that kind of a bright light, like this kind of typical indoor conditions and bright light. They were something that you really don't get with these current fonts with this kind of filtered output. And the one nostalgic thing is like this using the fill in flash and later on adjusting how much flash you eventually have in the output. I mean that was that was like the really thing I would miss I would I would really like to see in the existing smartphones as well. Absolutely. I would point you to you, had by the way, just entirely uh by the side, to my Symbian Museum. I've started a set of pages on my website where I could look back at classic, Nokia , uh and UI key, but mainly Symbian based smartphones, and of course the eight oh eight has a high l a big uh a big splot there also the N eight and the N eighty six I was the N eighty six before your time? No no no I was I wasn't there. I I when I s I started I might think my first phone was like in uh I was involved in the N70. I joined Nokia in 2004. So I didn't I wasn't part of the very first smartphone, mutta since 2004 I was I was involved in the image sensor part already. So I recommend listeners also go and check out my Symbian Museum. Lots of famous Yuha Uha um involvement smartphones. Sorry to d distract from all NCOS by uh going back in time. lovely to have you on. I hopefully we'll put lots of links in the podcast show notes to to I o and uh anything else you you want to links you want us to promote or pages, then do send me an email. Um thank you very much for joining us. It's been lovely lovely seeing you both. Yes, likewise. Nice to meet you. All right, bye for now. So there we go. I hope you enjoyed that and it gave you a glimpse and a taste of what's to come in the phone imaging world. Uh PSC will be back with eight nine six very, very soon. Ted and Joe carrying on excellent work keeping the PSC going, up to show one thousand and maybe even beyond. I'm really enjoying the the rebirth and um I hope you are too and I will speak to you hopefully on PSC nine hundred when I promise to appear again and perhaps in one of the upcoming PSC live, bye for now .