Jimmy Carroll: [00:01:05] Hello everybody and welcome to this episode of the Manufacturing Matters podcast, where we discuss the technologies and trends reshaping the manufacturing industry today. My colleague Winn Hardin and I have the pleasure of being joined by Ron Low and Pier-Luc Tardif of Airy3D. Guys, thanks so much for being here. I really appreciate it.

Ron Low: [00:01:22] Yeah, great to have you guys have us on this show. We’re really looking forward to it today.

Jimmy Carroll: [00:01:28] Awesome. Our pleasure. Yeah, so I’ll kick it off by asking for those who don’t know, tell us a little bit about Airy3D and what you guys do there.

Ron Low: [00:01:37] Well Airy3D is fundamentally an IP and software company. We’re a startup. We’re a Montreal-based startup. 2015 is when we started, and we’re kind of at the forefront of next-generation 3D sensing technology. So we basically allow CMOS image sensors to have the additional functionality of 3D through our specific manufacturing process that we provide, which is an optical encoder. The DepthIQ software stack on top of that. So it’s a really cost-effective, good solution for a problem or for a challenge in 2D or applying 3D in that scope.

Jimmy Carroll: [00:02:24] Yeah. So one topic that comes up a lot is ease of use. So how does Airy3D make 3D imaging systems easier to integrate and deploy?

Ron Low: [00:02:34] Yeah, well maybe we’ll do intros. I mean, I’m the senior VP of business development, and I sort of look after partner development and all of the marketing. So focusing on developing relationships with existing partners and new partners as we move forward, identifying new and interesting applications and use cases as well as verticals. So not just me, my team and I, which Pier-Luc is part of the team, we look after that side of the business. So Pier-Luc I’ll let you introduce yourself.

Pier-Luc Tardif: [00:03:06] Thanks, Ron. Uh, yeah. Pier-Luc Tardif, director of bizdev. So Ron’s my boss. I’m part of his team, and we’re very similar function. To go back to your question, in terms of integrations and how to make it easier to deploy, it’s very difficult to build 3D cameras it turns out. So whether you’re doing stereo and you require multiple sensors and lenses or you’re doing time of flight and you need illuminators or you’re doing structured light and you need pattern projectors, so it’s very difficult to build something that’s affordable, that’s compact enough. So what we do, like Ron mentioned, with just an add-on to the sensor and a software stack, we can make any normal 2D camera see in 3D in a pin-compatible format. What I mean by that is, if you just have a camera today that you like and you just swap the sensor inside for a sensor with our tech, you’re unlocking 3D without changing anything to the design of the camera or to the product itself. So you don’t have to go back to the drawing board. So I think in terms of ease of deployment, that’s really a big deal, right? You don’t have to go back to the drawing board and add an illuminator or add another other lens or other camera. So it really makes things easier.

Ron Low: [00:04:36] One machine vision customer actually called it retrofitting with additional features. And I kind of like the resonance of that because really what we’re doing is you’re taking existing built technology that you’re using, maybe you’re used to, maybe you already have implemented, done all the training, maybe already have it deployed in the field. And now the additional element of 3D is a really interesting add, and you don’t want to rewrite the book or completely start from scratch. So that’s a really compelling way of adding added functionality without completely, like I said, starting from scratch.

Winn Hardin: [00:05:12] And again, this is external to the camera housing itself, right? It’s not a gradient that’s applied directly to the sensor, correct?

Ron Low: [00:05:19] So it is actually the sensor that changes. So Pier I’ll let you go.

Pier-Luc Tardif: [00:05:23] Yeah, so it’s a very thin layer that’s placed directly on top of the sensor. On top of the micro-lenses. And it’s done at the tail end of the wafer manufacturing process. So you can almost think of it as a packaging option before you send the package, you can choose to add the layer or not. And we fit it to the sensor, so there’s no change in design that needs to be done to the sensor. So it’s a very standard process that is very well-known to the sensor maker guys.

Winn Hardin: [00:06:00] So is this done in sensor assembly during the packaging step or is this done at the die level?

Pier-Luc Tardif: [00:06:10] It’s on the wafer.

Winn Hardin: [00:06:11] Okay, like a specialized coating. And that gets back to an earlier question I was going to ask you, Ron, who it’s great to see you. We’ve known each other for so long. But you were talking about partnerships as your primary go-to market, and you mentioned you’re primarily an IP company, right? So Ron, tell us a little bit, so if people are interested in using your technology, you’re mainly working with and you’re selling to the primary sensor makers out there, correct?

Ron Low: [00:06:50] Yeah. So fundamentally, we’re working with partners. You know, we have two partners today, Teledyne e2v and Nes T, that we’ve partnered with. These are CIS manufacturers or CMOS image sensor manufacturers that are in the marketplace serving markets. They’ve identified our technology as being really an interesting add to their portfolios, to be able to address those markets but provide, like you said, the additional functionality of 3D.

Winn Hardin: [00:07:17] Right, and the Teledyne e2v partnership that was relatively recently announced. Is that correct?

Ron Low: [00:07:24] Relatively recent. I mean, we’ve actually got a product out into the market, Topaz5D, and it’s doing very well. Acceptance in the market’s high, a lot of interest, a lot of interest in different types of verticals. You know, this show is fundamentally focused on machine vision. And I think that’s an area that we really think is great fertile ground for this particular technology as people sort of explore past the basics of 2D camera technology and start to see use cases where 3D could really make a difference. But they’re also well entrenched in other markets, like medical. They are doing things in robotics and other areas. And then we also have other verticals, like consumer and automotive, which are also served somewhat by our partners. So we are kind of addressing a lot of markets. It’s a lot because it’s wide. But I think what people are finding is that 3D is starting to be a necessity in a lot more use cases than maybe they initially thought.

Jimmy Carroll: [00:08:30] Yeah, Ron, on that note, like one question that I wanted to ask you that I’ve just been thinking about lately and in the context of Airy3D, I think it makes sense to ask, do you think 3D imaging has finally come of age? For so long they say it’s a well-established, well-indicated technology with a large application base, but is 3D now democratized? You know what I mean?

Ron Low: [00:08:54] Yeah, I think in certain industries 3D was something that was well-known. I mean, stereoscopic cameras were well implemented in certain markets, like AMR and AGV. But I think what we found now is that 3D is kind of matured. And the fundamental challenges from certain types of 3D depth is not addressing all of the areas that have come up, whether that be shiny surfaces or there’s areas where you might have some challenges with dead spots or reflections. Min Z is a challenge in 3D in certain applications, even ToF, as good as it is, has some challenges. So what we’re seeing is 3D is kind of matured to the point where a monocular 3D solution in frame with no latency is really compelling because it kind of crosses some of the hurdles that have since been encountered in 3D. So I think the maturation of 3D as we see it today versus what it was in the last five to 10 years is why this technology is so compelling for a lot of people.

Winn Hardin: [00:10:08] So when we talk about cameras, we talk about the evolution of what the market is focused on, right? I mean, when we went from monochrome to color, there was the big concern there, what propelled it forward, color imaging, was price points. It was price points of the cameras as well as the computational ability to crunch that data in real time. And I know no manufacturer likes to talk about price points, because we’re not competing to the lowest possible level. But does this approach when you are eliminating active illumination, you don’t have choppers on ToF with better resolution. How much does price play into this? And then I would love to get deeper into, what are the trade-offs, because I think all the engineers in our audience are going to talk about you know, okay, well, we went from monochrome to color. We had a resolution hit. And so what are the considerations here? Price and considerations and benefits?

Ron Low: [00:11:07] So it’s not a race to the bottom. I mean, at the end of the day in some cases it’s a price challenge. But sometimes it’s actually a space constraint issue or even a use case issue where something needs to be miniaturized. That means that this solution is better. But clearly we’re saving componentry. We’re saving not only components and bill and material costs, but we’re also saving power. And power is a huge issue. And people want to make sure that they can, from a green perspective and from a cost perspective, save that as a big part of it. So yes, it’s less components, but we’re doing more with less, if that makes sense.

Winn Hardin: [00:11:49] Sure. So what are the pros and cons, trade-offs we’re making versus a stereoscopic or active illumination or ToF going to the diffractive gradient solution, Pier-Luc?

Pier-Luc Tardif: [00:12:00] Yeah, in terms of trade-off, Ron talked a little bit about minimal distance. So with stereo, the fact that you need this overlap between the field of views means that there’s a region with no overlap and means you can’t get so close and still get 3D. Seeing everything from one reference point means you don’t have that issue. So if you’re thinking any AMR that needs to get close to something, just to give you an example, like if you have a robot lawnmower, those things will have a very big buffer zones to whatever they see because if they get too close, they start being blind. So you can’t get your grass cut like 2 inches from the wall. So that’s an issue.

Winn Hardin: [00:12:53] That’s what weed whackers are for, Pier-Luc.

Pier-Luc Tardif: [00:12:55] Who has time for that?

Winn Hardin: [00:13:00] That’s the funnest part. Mowing your lawn. Because you just get to get your aggression out. Just whack things down.

Pier-Luc Tardif: [00:13:07] Yeah.

Ron Low: [00:13:08] Even 16-year-old kids are, I don’t know, they’re like $40 an hour. It’s not getting cheap to get your lawn mowed. Even your own kids won’t do it for free.

Winn Hardin: [00:13:17] No, they’re driving nice cars too, actually. So, respect. We all had more money when we’re that age. But not to digress. So Pier-Luc what are the other trade-offs we’re talking about? So proximity, standoff.

Pier-Luc Tardif: [00:13:29] Yeah, Ron did a good job of outlining a few of them. Space is a big thing. It’s very hard to build a very small baseline stereo system. And there’s applications where you need to be in small places. Uh, just as an example, if you’re examining someone’s teeth and you want to get a scan of their teeth in 3D, you don’t want something this big going into people’s mouths. So in the medical, for instance, where space constraints are a big deal, that would be another place where we shine. Another big deal is that we’re completely passive. We don’t need any illumination whatsoever. A lot of stereo systems out there will require to send some kind of IR pattern in order to get good depth. We don’t need that, which means it’s just another component you can remove. In terms of trade-offs, because you talked a little bit about the shift from monochrome to Bayer. Obviously, we’re adding a bit of diffractive optics on the sensor. So any optical engineer or physicist will know that diffraction means a little bit of resolution loss.

Pier-Luc Tardif: [00:14:55] So there’s a very slight impact on image sharpness. We’ve never had anybody complain. We have partners that use TDM, TDM being the optical encoder versions of the sensor to do barcode reading, and they don’t have any issues. In terms of the resolution of the sensor, you’re getting, there’s no loss there. What I mean by that is if you have a 2 megapixel sensor, you’ll still get a 2 megapixel raw image and a 2 megapixel image after your ISP. So it’s not like something like Quad Bayer where you need, basically, if you have a 50 megapixel Quad Bayer image, you’re getting a 12.5 megapixel image at the end of your ISP. We don’t have that down-sampling effect. But yes, there is a small loss to image sharpness. If you’re talking MTF 50, if we really want to get into the weeds, you’re talking maybe 5%, which is really not a big deal. Same goes for the quantity of light that gets to the pixel.

Winn Hardin: [00:16:06] I was just going to ask if photon QE got affected.

Pier-Luc Tardif: [00:16:09] Again, you’re talking maybe a 5% loss. So it boils down to do the benefits outweigh the trade-offs. And you’re getting 3D for a very slight loss of MTF. So to us it’s a no-brainer that it brings a lot of value.

Winn Hardin: [00:16:26] Absolutely, absolutely. And I actually have a follow-up question. Jimmy, go ahead man. I’ve been hogging. Go ahead bro.

Jimmy Carroll: [00:16:33] No it’s cool. Um, yeah. It’s kind of a direct follow-on to what you were talking about Pier-Luc. So like with value props, benefits like space and cost in mind, what are some of the other applications where you’ve either seen your technology deployed and where you could see it adding value? And I know you’ve mentioned some already, but I just wanted to go a little deeper on that.

Winn Hardin: [00:16:52] That’s where I wanted to go too. Just let me add, because it sounds like we’re talking about a lot of embedded applications, right? Not your traditional in-factory machine vision applications, although obviously the technology would apply.

Pier-Luc Tardif: [00:17:02] Yeah, there is some there. But yeah, there’s just a lot of verticals. Automotive is really a big deal for us.

Winn Hardin: [00:17:12] We’re talking autonomous primarily as opposed to assembly in this particular case, correct?

Ron Low: [00:17:18] But actually both. So we’re actually in the manufacturing side of it as well and the robotics side, I guess you would really classify that. I mean, it’s robotics-automotive. But really what we’re talking about in automotive is sort of in-cabin applications and out-of-cabin applications.

Winn Hardin: [00:17:33] Right. Okay. And the 3D being critical for pick and place and other robotics. So that’s why vision-guided robotics is such a such a key component. 3D vision is almost always needed in that, right?

Pier-Luc Tardif: [00:17:43] For sure. And about pick and place, in a lot of these applications with robot arms, what you’ll see is, you’ll have your area where the arm works and you’ll have a structured light sensor on the ceiling that looks down and looks at what the robot’s doing. But if you have something that’s a 2D camera the size of my thumb that you can make see in 3D, well all of a sudden, you don’t need that on the ceiling. You can have it on the tooling of the robot. So it can go very, very close to the objects and see in 3D. So yeah, again, space is, having something that’s compact means you can put it at the end of the . . .

Ron Low: [00:18:30] Yeah, the acronym EOAT or end-of-arm tooling is really interesting for this kind of application, especially when you’re getting close to objects that really are challenged with either the machinery in its own way or where you need to have some sort of level of real sensitivity to be able to get something close to something. As we start to, like I said, in machine vision expand the use cases to the more difficult stuff. I’d like to say that maybe the easy stuff’s been not done, but I think it’s been covered. And I think now what people are saying is like, okay, well, it can do that. How about this? Can I add this level of functionality within my industrial setup? Can I add that to it or can I do that, or what’s even maybe more interesting is that you have one tool that maybe does multiple things. So not robot per task but one robot per multiple tasks.

Winn Hardin: [00:19:29] Multiple regions of interest. Yeah, you could just see where that occlusion factor, the lack thereof, becomes so critical. You know, if you’re doing any kind of gauging of milled pieces or parts, if it’s end-of-arm tooling and you don’t have to carry all that weight, which then adds to cycle time and all these other, you can pick exactly the perspective you want. And especially if we’re leveraging software packages either on the robot or the vision side that are using AI to optimize that perspective and location for a particular measurement or ROI. And I think you would agree that’s one of the trends we’re seeing over and over in machine vision, right, is that you don’t have one robot per task anymore. I’ve seen hundreds literally of inspection places on server towers or other large components, even inch compartments might have 10, 30, 10, 20, 30 different inspection areas.

Ron Low: [00:20:20] Right. And we haven’t really talked about compute yet. And we can. But the other element is that we’re compute-light, but also we can be customized-compute. And what I mean by that is that if you want to run the 2D at 30 frames per second, well, you don’t need to run the 3D at 30 frames per second. It can be a lot less. It can be one frame per second if that’s the requirement for the use case that you need. It can almost be turned off and turned on when you need it. It can even be almost alerted when it’s required. So from a compute perspective, that can be really attractive because obviously software is a big part of our tool chest. So we also can provide some customized elements to that, which can be really interesting for customers.

Winn Hardin: [00:21:08] So when you’re providing it’s not just a coding, right? You’re doing it either an FPGA or some other computational element as part of the solution. Did you work with the camera maker or the customer, if it’s going to be in for an embedded solution, to kind of help them identify what the computational elements requirements are? How does that play out?

Pier-Luc Tardif: [00:21:29] So we’re very agnostic when it comes to the platform. We don’t design that. We do provide the software stack that goes because basically the optical encoder adds information into the image. And then depth IQ takes that information and converts it into 3D. So what we’re doing with our partners is getting a pulse on what SOCs are attractive out there. And then we port our algorithms onto those platforms so we can offer a buffet of systems on which our algorithms can be deployed.

Ron Low: [00:22:11] So your FPGA, GPU, NPU, I mean, there’s really no system that we cannot run on. I mean, there are some constraints certainly in some of the smaller-sized Raspberry Pi-sized type solutions would be challenging. But you know, that’s no different than any other vision system. But I think there is, like Pier-Luc says, we’re agnostic and we are always continually improving the capability of the IP. So we’re becoming lighter and lighter as we move forward, as we advance our technology. So it’s not a heavy piece of software.

Winn Hardin: [00:22:55] Okay. And you mentioned before our call that we’re across a lot of . . . RGB as well but also IR to a certain extent in terms of effectiveness, ability to extract 3D. So, I mean, am I correct in saying that you can help a camera maker basically make a monochrome high-resolution camera with lower processing. You could be color. The same camera could be color, 3D, and monochrome, depending on exactly what they’re shooting at that moment or what the needs are of the application. Is that a fair statement?

Pier-Luc Tardif: [00:23:25] Yeah, absolutely. I mean, the rule of thumb is if the sensor can see it, we’ll get depth from it. So as long as the QE of the pixel in IR is sufficient, we’ll be able to get depth from it. No problem.

Winn Hardin: [00:23:40] Very cool.

Ron Low: [00:23:41] Yeah, back-side illuminated, front-side illuminated, global shutter, rolling shutter. Um, we haven’t actually come across a sensor that has been made that hasn’t been able to be 3D-enabled. So I think that’s interesting for us as we move forward because I think our manufacturing process is outside of the inner workings of it. So it’s icing on a cake, which really I think enables it and allows it to be used in a lot of different areas. There’s some that frankly we haven’t even come across yet. Shortwave infrared would be interesting. There’s some other stuff that’s out there that’s yet to be developed with the CIS vendors. And we’re always interested to see how 3D can add to the functionality.

Winn Hardin: [00:24:28] I was just wondering if this was silicon-based in terms of responsiveness, spectral responsiveness. But it sounds like if you’re talking short, possibly long wave, then we’re talking about inGaAs, other sensor types could still leverage this technology. Is that correct? Could you optimize for a much broader spectrum or is there any limitation?

Ron Low: [00:24:49] Well, we haven’t come across one yet. So I guess the thing is, we’d have to test it. But so far, so good. We’re batting a thousand.

Winn Hardin: [00:24:57] Great. That’s a call out to all the remote sensing and aerospace companies. See if you guys can trip these guys up a little bit. That would be cool. 

Ron Low: [00:25:08] We accept the challenge.

Winn Hardin: [00:25:09] Exactly, exactly.

Ron Low: [00:25:12] 110 mile an hour fastballs, I’m not so sure.

Jimmy Carroll: [00:25:16] Well, a little further to the ground in manufacturing processes and then ending in other industrial processes, right, I could see how this ties into a theme that’s popped up over the last couple of years, which is flexible manufacturing or dynamic manufacturing or whatever in a logistics facility, right, where the same camera could be doing high-speed barcode reading or based on what’s coming down the line, maybe 3D dimensioning. So it just adds a lot of flexibility to these systems. And you know, since the podcast is called Manufacturing Matters, I did want to ask some questions within the realm of the factory floor. As far as pain points in manufacturing, manufacturers today are facing, beyond just the labor shortage, how can your technology and other machine vision technology out there help?

Ron Low: [00:26:09] I mean, with the advent of onshoring and the geopolitical situation, we’re seeing a lot more manufacturing coming back to North America or certainly the continent. I think there’s an investment that’s coming back into it to a certain extent. I mean, latest interest rate reductions as well has meant that capital expenditure is a thing that could maybe be looked at again as we see that manufacturing requirement grow. And I think there’s some numbers that are out there, I can’t cite any, but I know that manufacturing is starting to become a challenge because of labor shortages, as you mentioned, Jimmy. But I think smarter and autonomous systems allow you to be more efficient. And if you can widen the scope and capability of these systems to do more things, you increase your efficiency yet again. So now all of a sudden you become much more cost-effective. And your manufacturing costs while being in North America can compete globally. So I think that’s really interesting. Also, when you talk about safety and precision, these are elements that get added to the arsenal of being able to be enhanced by 3D.

Ron Low: [00:27:19] And so now you have worker security, worker safety. You’ve got maybe longevity of certain people in certain types of jobs that are enhanced because they can work longer in terms of their life span, because the hard part of the work is taken away from a robotics use case. So I think that all adds to it. Um there’s government funding as well. You know, the CHIPS Act and other government funding is starting to fuel technology advances within the marketplace. And that is only also going to fuel more CIS manufacturers to look and broaden their product portfolio. And that’s also interesting. So I think that’s a big convoluted answer. But frankly there’s a lot of elements that I think are at play which all feed into the ability that I think those pain points are starting to become less and less. I mean, don’t get me wrong, there are still pain points, of course. But I think we’re starting to see areas where technology can really address some of that.

Winn Hardin: [00:28:33] If it was always easy, right, life would be boring. I’m sorry. What were you going to say, Pier-Luc?

Pier-Luc Tardif: [00:28:36] One thing I would like to add is I think in terms of automation, we’re already quite good at environments that are very deterministic, you know production lines that always stay the same. But if you’re making machines smarter with AI and now 3D vision, you can deploy in environments that are not deterministic, that change from day to day, that have people moving around. So all of a sudden you’re not pigeonholed into these very rigid places that never change. And you can basically, yeah, just make machines smarter.

Winn Hardin: [00:29:14] Kind of the definition of that whole embedded application space that I think is going to probably be such an important part of Airy3D’s development. Ron, you bring up a really interesting point that I have to follow up on. You mentioned the CHIPS Act. You know, and we think about this and the implication from traditional semiconductor, computational elements. But how is this going to impact CIS here in the U.S.?

Ron Low: [00:29:39] So TSMC is obviously one of the manufacturers of semiconductors that’s building large-scale semiconductor plants in the U.S. But that’s going to filter down into other nodes. So CIS sensors don’t need 3 nanometer-, 5 nanometer-capable nodes. But there is still a huge demand for 40 nanometer and 30 and below. And those will get freed up, I think, by the fact that you’ve got larger facilities and more facilities globally covering some of that requirement and some of that allocation. So I would think that the CHIPS Act is only going to enable and help other people that are in that marketplace that are manufacturing. We know that Samsung is getting funding. You know, Sony is doing some joint funding. We’ve got other manufacturers that are North American-based that we believe will also benefit from that. So I think just in general also when you’re talking about the CHIPS Act, you’re also talking about a capability that’s going to be increased in terms of people are going to start getting trained and learning how to do this, and there’s going to be more people and there’s going to be more of a requirement for those people. So I think that’s also going to help push the volumes that are out there. And frankly is technology going to go away? I think people are just going to continue to miniaturize and continue to want more things from their tools that they have, whether that be private or industrial. And that I think is going to be more chips going forward.

Winn Hardin: [00:31:20] Yeah, it’s hard to imagine where chip demand somehow plateaus. It doesn’t seem like the future I know of unless there’s some sort of biological computing element where it can start growing itself. But that’s beyond my scope. So I think that’s a very safe statement. And this type of technology I think is critical to that. Some of the trends you were talking about earlier when we were talking about general labor, where we’ve got anywhere from 13% to 20% to 25% gaps in assemblers, machinists, other key components, right? So automation is, as we like to say, is never taking jobs. What it’s doing is it’s making existing operations more efficient, which inevitably allows people to hire more people and take on more capacity. The companies that adopt this technology are the winners, and their employees are the winners too, without question. But the ability to do multiple things at one time just seems super critical as we go forward, right, and having cameras that have more utility that are not just tasked with one element, and that also seems to be super critical again to the growth of embedded machine vision. And which is so exciting to me because, Ron, we’ve been in the business long enough where it was always a traditional discussion of what’s on the plant floor, what’s going on there. And now we’re really starting to see our technology get out there into the marketplace, into the wide world. So that excites me as someone who’s been around watching this for a long time.

Ron Low: [00:32:51] Yeah, I absolutely agree with you, and we’re fortunate. You know, we’re in an industry that, okay, I think the fundamental foundations of it are older, but we’re not so entrenched in that history that we are not all excited about what’s coming. And I think the future certainly of 3D sensing is huge, and that’s why it’s such an interesting field to be involved with, because machine vision is changing and changing rapidly. And I think that’s an exciting journey to be part of.

Winn Hardin: [00:33:26] Right. Beautiful. That’s a good closing note. But before we slow down this show, Pier-Luc, we’re going to have to ask you real quick about the sword behind you on the wall.

Pier-Luc Tardif: [00:33:38] So for any Legend of Zelda aficionados or enthusiasts out there, this is the master sword. Uh, if you want more geek stuff, I do have a light saber as well.

Winn Hardin: [00:33:53] Right next to a sweet Iron Man mask. Respect. And somehow Jimmy gets morphed in between there. So there’s an analogy. There’s some sort of metaphor going on in that back wall.

Pier-Luc Tardif: [00:34:04] Yeah.

Winn Hardin: [00:34:05] That’s awesome.

Ron Low: [00:34:06] Yeah, is that Jimi Hendrix in 3D? I don’t know. It doesn’t look like it.

Pier-Luc Tardif: [00:34:10] Not yet.

Ron Low: [00:34:11] Right

Jimmy Carroll: [00:34:13] Actually a point cloud.

Pier-Luc Tardif: [00:34:14] Jimi Hendrix point cloud. It’s the next thing we need.

Ron Low: [00:34:20] It’s going to be our new website page.

Pier-Luc Tardif: [00:34:21] I call them worthy investment. But my wife disagrees.

Winn Hardin: [00:34:26] That’s why we keep them in the den.

Pier-Luc Tardif: [00:34:29] That’s why it’s in my office. Doesn’t bother anyone.

Winn Hardin: [00:34:34] What she don’t know. Well, what she doesn’t see, she can’t yell at you about every day.

Pier-Luc Tardif: [00:34:38] Exactly.

Jimmy Carroll: [00:34:42] Guys, what else haven’t we talked about? Where can people find you guys next?

Ron Low: [00:34:53] Well, I mean, you know, our website is going through changes, so that’s always a good place to start. We’re obviously heavily involved with different industry events. We were at Automate earlier this year. We will be at a big show, a big camera show, which I’m sure you guys are aware of, which is Vision 2024 in Stuttgart, in a couple of weeks, with our partner, actually, Teledyne e2v as well. We will be on their booth. So we’re everywhere we think we need to be. But you know, it’s a large marketplace, but you know, we think we challenge a lot of areas by providing a technology that really fills in gaps. So you know, we feel if you want to talk to us, please reach out. You know we’re happy to talk to people that have challenging scenarios within what they’re planning to do. And we like to be challenged also. So we don’t have all the answers, but we do have an awful lot of smart people in our company, which is great, myself excluded. So it’s good to have some people around us that can can drive it forward. And that’s what’s exciting.

Winn Hardin: [00:36:10] Any visibility for Q1? Are you guys going to Photonics West? I could see you guys playing a nice position there. Anything else beyond Stuttgart?

Ron Low: [00:36:19] Uh, we’ll be at CES actually. So there’s a little bit of an automotive component there that we think we will be part of and other things. I would think that we’ll also be part of the A3 group. There’s an A3 event, Winn, that you and I were at last year. So I think we’ll possibly see each other there. And yeah, Photonics West and the West Coast is full of different types of events. I guess the one thing that’s challenging is that there are so many, and they’re starting to become really quite specific. So we want to make sure that we maximize our travel dollar. Our CFO wants to make sure that’s the case. But we want to be where we think we can make a difference. And so if you’re an event holder and you think our technology — and you’re tuning into this stream — and you think we could do something that would be really interesting for your target base, then reach out, please.

Winn Hardin: [00:37:16] Yeah, I’m sure there’s a lot of events out there would like to have some technical presentations on this technology and really get their elbows in deep to your tech data. So I know that would be exciting.

Jimmy Carroll: [00:37:29] Yeah, for sure. And not only that, anyone who’s up for challenging Ron and Pier-Luc and everyone else with their difficult applications, they welcome it and look forward to any comments, questions, or suggestions you might have. You can learn more at Airy3D.com or find these guys on LinkedIn, or if you like you can reach out to us at manufacturing-matters.com, and we’d be happy to pass along those questions. So guys, thanks so much for taking the time. Really appreciate it. It’s been a pleasure.

Ron Low: [00:37:56] Yeah. Winn, Jimmy, I really appreciate you setting this up. It’s great to talk a little bit about our technology and share what we can do without being too salesy. But I think we addressed some of the challenges in the market, but you guys do a phenomenal job of touching so many different sectors. So it seemed like a great platform. And I really appreciate your time today. Thank you.

Winn Hardin: [00:38:15] Thanks, Ron. Always appreciate it. Pier-Luc, it’s a real pleasure to meet you.

Pier-Luc Tardif: [00:38:19] Likewise. Thank you for having me. This was fun.

Winn Hardin: [00:38:22] Okay. We’ll see you later on Twitch. And if anybody wants to check out our past episodes, go to manufacturing-matters.com. To see past episodes, look at any of your favorite podcast platforms. We’re all over the place. And until the next time we get to see you, have a great day.