Good day, everyone, and Welcome to the PDF Solutions, Inc. Conference Call to discuss its financial results for the second quarter conference call ending Sunday, June 30, 2024. At this time, all participants are on a listen-only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during the session, you will need to press star one one on your telephone. As a reminder, this conference is being recorded. If you have not yet received a copy of the corresponding press release, it has been posted to PDF's website at www.pdf.com. Some of the statements that will be made in the course of this conference are forward-looking, including statements regarding PDF's future financial results and performance, growth rates, and demand for its solutions. PDF's actual results could differ materially. You could refer to the section entitled Risk Factors on pages 16 through 36 of PDF's annual report on Form 10-K for the fiscal year ended December 31, 2023, and similar disclosures in subsequent SEC filings. The forward-looking statements and risks stated in this conference call are based upon information available to PDF today. PDF assumes no obligation to update them. Now, I'd like to introduce John Kibarian, PDF's President and Chief Executive Officer, and Adnan Raza, PDF's Chief Financial Officer. Mr. Kibarian, please go ahead, sir. Thank you for joining us on today's call. If you've not already seen our earnings press release and management report for the second quarter, please go to the investor section of our website, where each is then posted. Before Adnan discusses the financials in detail, I have some comments to make about our observations for the second quarter and our view for the market for the remainder of the year. Our bookings in the second quarter were lower than the strong Q1. Due to the nature of some of the larger contracts, we expect lumpiness in any given quarter and therefore find it meaningful to look at a rolling average. Since our bookings started improving in Q4 of last year, we've been building backlog, which will support our future growth. The bookings in the quarter are mostly with customers that are either starting to deploy new systems like Sapience Manufacturing Hub and MLOps, or expanding the usage of our platform. In both cases, we anticipate many of these contracts to lead to expansion business in the future. Notable deals in the quarter include a large contract for Exensio Process Control for an advanced logic fab. Contract for initial deployment of Sapience Manufacturing Hub for a large logic manufacturer who's doing a significant SAP S/4HANA deployment. Successful completion of this initial phase is expected to result in follow-on, much larger, more significant multiyear license for SMH, tying all their manufacturing to ERP system to facilitate new levels of productivity. That same customer, having already deployed Exensio in advanced packaging, is also entering into a contract with us in the quarter to pilot Exensio for wafer fab analytics. We closed our first contract for MLOps, an AI-based product we announced in Q4 of last year. This contract is for a large fabless customer that is beginning their journey to deploy AI for test, testing of products. We anticipate successful application of AI for this, use will result in their expanding the use of AI for most of test. A number of customers also expanded Exensio Cloud usage. While increasing the annual run rate of these contracts, these expansions also set up for a larger renewal, some of which we anticipate occurring in the next few quarters. Finally, bookings for Cimetrix Connectivity runtime licenses showed modest improvements in Q2 over Q1 as our customers' equipment shipments increased. Overall, given a strong backlog and business model where most of our revenue is typically ratably recognized, we continue to deliver strong results in revenue and earnings. We were pleased with the business results in the quarter as it demonstrates the strength of our business model. Turning to DFI, as we stated before, we have two machines at one customer and another machine at a second customer. A third has the right to send us wafers this year for us to analyze on the eProbe machine in our facility while they build their new fab. The machine will be shipped to them when the fab is ready. For the first two customers, usage in Q2 was very high. What is clear is that the direct scan application of the eProbe has very unique capabilities that we believe are valuable in bringing up logic product yields and eventually to control production of those products. In both accounts, we've begun discussions about expanding the number of machines. We anticipate those discussions may take the next couple of quarters to conclude. Now let me turn to discuss our view on the environment and our perspective on the second half of the year. As we talk with customers, our customers about their business, some are experiencing weakness while others are growing. As a result, we believe that for the overall semiconductor market, growth will be unevenly distributed. It won't be the case that a rising tide lifts all boats. With that said, our engagement with customers remains high, driven by fabs developing advanced logic processes such as 2nm, fabless customers deploying advanced test control software, often with AI/ML, to augment conventional test methodologies, and companies engaged in digital transformations attempting to leverage data, whether that is IDMs, fabless foundries, and equipment vendors. Given these trends and strong customer engagement, we continue to expect revenue growth for the second half of the year to be about 20% over the same period a year ago. I want to thank all the PDF employees and contractors for their efforts during the first half of the year. Now I'll turn the call over to Adnan, who will review the financials and provide his perspective on our results. Thank you, John. Good afternoon, everyone, and good to speak with you all again today. We are pleased to review the financial results of the second quarter and to bring you up to date on the progress of the business. Our Form 10-Q has also been filed with the SEC today. Please note that all of the financial results we discuss in today's call will be on a non-GAAP basis, and a reconciliation to GAAP financials is provided in the materials on our website. For Q2, our total revenue was $41.7 million, essentially flat versus the same period a year ago and up slightly versus the prior quarter. Analytics revenue was up 3% to $38.1 million this quarter, versus $37.1 million for the second quarter of 2023, and represented 91% of total revenues this quarter. The growth in our analytics revenue came from increased usage and upsized renewals by our extensive customers, as well as an uptick in our Cimetrix runtime licenses. As John said, we are excited about the level of engagement with our customers during the quarter, including Exensio adoption by a leading-edge fab customer, expansion of Exensio deployment by multiple merchant semiconductor customers, extension with a cloud provider for the internal use of Exensio, and an additional win on the Sapience Manufacturing Hub with our partner, SAP. We're also pleased with the engagement activity for our DFI system and eProbe machine, and you will see us investing further to continue to address the market needs. During the second quarter, revenue contribution from Integrated Yield Ramp was $3.5 million, down $0.9 million or 21% compared to the same quarter a year ago, driven by lower gain share from our Asian customers as a result of the low volumes. We are pleased with our backlog, which grew in the first half of this year from $229.8 million at the end of December 2023 to $243.2 million at the end of this quarter. The trends John and I have been discussing and the level of customer engagement leads us to believe we will grow our backlog in the second half of the year as well. We reported gross margins of 75% for the quarter, up versus both 72% for the last quarter and 74% for the same quarter of the prior year. We are pleased with our gross margin performance for the quarter, which is in line with the long-term target financial model we shared at our Analyst Day and user conference last year. On the operating expense side, our expenses for the quarter were slightly down versus the prior quarter, driven by better utilization of our headcount resources, primarily in R&D, while SG&A expense was essentially flat compared to the prior quarter. For EPS, we reported a profit of $0.18 for the quarter, improving from the $0.15 we reported for the prior quarter. We ended the quarter with cash and cash equivalents of $118 million, compared to $123 million for the prior quarter. We generated a small operating cash flow for the quarter. During the quarter, we used cash primarily for investment to support the development of our DFI system, to address the market need and the build of additional machines we mentioned earlier. As we look to the rest of the year, we remain committed to our prior guidance for the year, with revenue growth returning to our 20% long-term target for the second half of the year compared to the matching prior year period. With that, let me turn the call over to the operator for Q&A. Thank you, Mr. Raza. Ladies and gentlemen, if you have a question at this time, please press star one one on your telephone. If you're using a speakerphone, please lift the handset before asking a question. Please stand by for our first question. Our first question comes from the line of Blair Abernethy with Rosenblatt Securities. Your line is open. Thanks. Nice quarter, guys. Thank you, Blair. Just two questions, I guess for me. Just one on the DFI and the other one on the MLOps system. So, on the DFI, you talked to, you know, at length about it last quarter. I just want to see what sort of—what do you see as having advanced or changed over the quarter, and what is your manufacturing plan looking like? I see the CapEx is stepping up. To give us some sense of, you know, the ramp-up in capacity to be able to deliver equipment on the DFI. And then on the MLOps system, can you give us some sense of, you know, how big are these transactions? It seems like it's, you know, from the time of introduction of the product to actual sales, a pretty short selling cycle. Just maybe talk a little bit about that. Sure. So on the eProbe, what's developed in the quarter? As I said in my prepared remarks, Blair, usage was very high at both customers, and we started, you know, we have pretty good customer base in the fabless community, too. And so at least we started hearing from the fabless community about how they've been seeing the results, too. It's very, because the machine is very good at understanding the relationship between the design and the manufacturing yields, and most inspection tools understand that you compare one inspection result to another to find problems. This understands where it is on the design. So they're able to, therefore, the manufacturer can talk to the designers about what specific design capabilities they're seeing, et cetera. I think that's probably why they've communicated with their customer base about it. So that's always good when you, you know, you hear it from our customer's customer. Yep. And you know, as we've had dialogues, I think we're starting to get some understanding or indication from at least the first two customers about what would be the potential number of machines they need. We don't think in the short term we can meet both of their needs. They will probably. It will probably spread out throughout all of 2025 in order for us to. If they were both to come in at, you know, what they say, and there's a lot of ifs there, so let's not go and get too far ahead of ourselves. But we are, as you can see from our CapEx buildup, trying to mitigate as much as possible, you know, contingencies around being able to respond to demand. You know, the typical lead time for machines like this is quite long. So, you know, we're trying to do our best to pull it in, but, you know, you can pull it in a matter of a couple of months. You can't go and make this an, you know, an instant lead time. So I think the lead times for this are not much different than the lead times for other machines of similar complexity, which is, you know, nine months to a year, kind of like time frame. So, you know, we are very excited about where we are right now. We do think, you know, besides the opportunities with these customers, we think it speaks more broadly. We've had a lot in customer requests in memory, in other areas around pilots, because of this kind of unique capability of the machine to kind of know exactly where it is in the design and apply appropriate stimulus on each part of the design. So, you know, that's kind of my answer on the DFI eProbe. So move on to the MLOps. You know, again, we're seeing with customers as they're doing more and more advanced packaging, there's many more test insertions, and they want to be able to feed models extracted from earlier test results up into later tests to get better better performance, better test time, better quality screening, etc. And this has been the first application for this. This is a customer we started working with. You know, it seems very short from announcement, but we started working with this customer before the announcement. As we were getting close, it was a kind of an early lead customer. The initial deployment is on a relatively small number of testers. That's why I said it's really only a small portion of their total production. And as we believe, as you would roll it out across a larger number of testers, as they would apply it to more and more products, it could be a relatively meaningful contract. At this stage, it is a modest contract. It's not super huge, but it is, you know, again, I would say on, you know, what would amount to being a couple of percent of their testers. A really, a very complex product. They want to prove this methodology works out for them. I expect them to roll it out on more as they gain success with AI at test. Okay, great. That's thanks for the color, John. And maybe just to add on, just following on the DFI comments, what should we expect in CapEx going forward? I know you've got lots of cash, but you know, $5.3 million this quarter, typically, you've been running $2 million-$3 million a quarter. What should we be looking for there? Yeah, very reasonable question. You know, to be honest, when we were ramping up DFI, you know, many quarters ago, we were also spending more than what we have in the quarters before this one. So look, I mean, for the next couple of quarters, at least, we think CapEx probably stays at similar levels. You know, as John said, this is a pretty unique time for us with the engagement with multiple customers on leading edge, and we want to be making sure that we are well positioned to take advantage of that opportunity. So probably similar to where we thought Q2 come in is a fair estimate. I think the thing to keep in mind also is, you know, look, we've been operating cash flow positive for a long, long time, and we intend to stay that way, as well. You know, in between the other uses of cash, we'll do from time to time, as the opportunities present are going to relate to, for example, share purchases, which we've done, for example, in Q1. Okay, great. Thanks very much, guys. Sure. Thank you. Please stand by for our next question. Our next question comes from the line of Gus Richard with Northland Capital Markets. Your line is open. Yes, thanks for taking my question. Nice quarter. Sounds like you've got an exciting outlook. I was just wondering on the DFI and your engagement with your two customers, what is the use case? Is it yield ramp? Is it, you know, bringing up a new product, or is it actually in fab in production? Yeah. So, so far, because it's been applied to very advanced nodes, Gus, it's been used for bringing up nodes and bringing up specific products. So, you know, each product uses the process a little differently. The design layouts are different. You know, we used to refer to these things in the industry as a systematics or something unique about the design, but now the process windows are so tight, there's always something specific about every design. So they've been, I think, across the- It's a couple of customers, you're using it on many different designs as they come into a node. It, you know, we believe that, you know, the even though the initial use is there, you tape out many designs in production for a long time. So it's kind of give you a first kind of use level, even in a production fab, not just a development fab. And we seem to hear that from the customer base. And over time, we believe it results in a control application as well, just because these, you know, the marginalities, the process windows are so tight that the need to monitor will remain. So early on, I think, you know, what's been happening over the last year, I'd say, or six months, has been really around bringing up products. I think as you transition into 2025, I suspect that it ends up being not just to bring up the products, but to control the technology. Got it. And then, I'll just stick with DFI. Can you add a little more color to your build plans for next year? And I'm assuming that the CapEx, because you effectively lease these products, the spending on building the tools and the various assemblies and with your subcons is actually CapEx. Yeah. So, build plans. We are ramping up to be able to build, on, you know, more. Somewhere, you know, I think somewhere between four and eight a year would be a reasonable assumption about what we would do in the short term, on a four-quarter basis. We believe our suppliers have the ability to do more than that, you know, be able to build more, but I think this is maybe our next away point. In terms of, the... You're right, that we've been s- the m- machines have been provided on a subscription basis, so, it is capital, it's cap- it's CapEx for us. You know, over time, I think both, we may, we may find, we may adjust the business model with customers at some point, and, the machine may be purchased and the software subscribed. On top of that, I think there's still some, flexibility on both the customer's mind and our mind about what's the optimal way. It kind of gets back to your first question, Gus. If you're gonna run it forever, you know, for the life of the node, would they prefer to purchase the equipment and subscribe to the software? That may be the case. You know, so we're, we're, you know, our customers are a factor of hundreds to thousands times larger than us, so we're gonna listen to them about what's the best way to, work with them in the business. You know, I think over time, the capital may come off our balance sheet if they were to purchase them, particularly if the control application is there. Got it. And then the last one for me is on the MLOps. You know, clearly, there's an expansion of chiplets and, you know, multi-die packaging, if you will. And, and I was just wondering, are those two related? And, and if so, is, are you starting to see increased interest at OSATs? They are related. The interest is coming from the product companies. The know-how about, you know, chiplet matching is really the responsibility of the Fabless company, because or the product group, because they own the test program. And they know how to interpret that, how to build a model to interpret that result effectively. And they know how to, let's say, reduce test time downstream or add additional tests or match chiplets better, so you get an overall system performance that's better. This is one of the motivations, you know, for this customer as well as others that we've dialogued with. It is also having us go back and look at our Exensio network. You know, we've made an investment in having our machines at the OSATs connected to their testers, so they can push data from the cloud. MLOps really allows them to manage all of that data traffic up and down, their manufacturing flow in order to tie, you know, initiate running of models that, let's say, extracts features for upstream tests to be ready downstream, so they can combine that, extracted feature from the upstream test with their testing that's going on real time to make, let's say, a binning decision or a sub-binning decision, et cetera. So yeah, you're right that it is very much related to chiplets and complexities on testing as a result of that. The OSATs have an important role to play because you need to integrate with their MES systems. Customers need to be integrated with their SAP system because they need to know where the chips are going and, you know, therefore, where the data needs to be sent. So it does bring out the overall system requirements that are needed, but the buyer and the user of it is really still the Fabless community more than the OSAT today. I think it'll stay that way, too, Gus. Okay. All right, that's it for me. Thanks so much. Thank you. Ladies and gentlemen, as a reminder to ask a question, please press star one one on your telephone. Please stand by for our next question. Our next question comes from the line of Christian Schwab with Craig-Hallum. Your line is open. Thanks for taking my question. So can you give us an update? You've been working with for some time on a meaningful semiconductor producer who's now going through, you know, a tremendous mess, for lack of any other description. Can you kind of give us an update of what the revenue opportunity over a multi-year time frame, you know, could be with that customer now that things in production could be readjusted? Yeah. So, Christian, thank you for the question. We're always, you know, very respectful of our customers' proprietary information and, you know, their, what's going on any given customer. So we won't comment on specific customer per se. But I can tell you in general, though, right, our technology is used to make, you know, to help customers be more efficient and more effective. So, you know, we always are mindful of the economic situation every customer is going through and think about, you know, what's the best way to work with them. But often, our technology is very important for customers to drive transformation. And so we look at these as opportunities often for us and the customer to be more effective in how they use our systems. And often it builds, you know, over larger business with us over the longer term. Sometimes, you know, you don't know if the short term it does do, and, and then that's true for every case when we're in these situations. In general, you know, our technology is very important for being much more efficient in manufacturing, and I think our track record of, of, being instrumental for customers in change management is, is quite long. Put another way, do you think this opens up an expansion of opportunity where things could happen faster than previously expected then? You know, our customers that are trying to move to advanced nodes, there's, I think the complexity of the technology is opening up opportunities. When our customers are really looking for that to happen now, and often when customers are going through transformations, they're looking for that to happen now. I believe our systems are increasingly valuable for those customers. So we look for ways to be able to deliver value, mindful of the fact that, you know, when customers are challenged economically, we have to also have to think, sharpen our pencils and think of how to be flexible as well. Fantastic. Great. No other questions. Thank you. Thank you. Please stand by for our next question. Our next question comes from the line of William Jellison with D.A. Davidson. Your line is open. Good afternoon, and thanks for taking the question. I wanted to start out by asking, amongst your existing Exensio customer base, what you're seeing with respect to trends of adopting the next incremental module for them. What are you seeing amongst those folks? Yeah, that's a great question. Well, thank you for the question. In my prepared remarks, I talked about drivers for the business, and two of them really kind of point to the drivers that we see at customers. You know, the first one I talked about a little early was more automation on test, that MLOps opportunity is, you know, one that I described. A lot of times, as I said in my prepared remarks, it's really being a much more sophisticated test and applying—going from applying rules to applying models, usually ML-based models or AI models, to be ready for chiplet production, you know, more efficient in driving quality, et cetera. The second I labeled broadly in my prepared remark is digital transformations, and we really see that's really driving two aspects of our business. The SMH, the Sapience Manufacturing Hub, really, which has really got, you know, the Exensio database inside it, is the way of connecting this, you know, as our partner SAP says, the shop floor to the top floor. When customers want to transform their business, they want to be able to act on whatever AI, ML they apply. Our customers that have been deploying ML models, one of the things they recognize is, I need to know from my ERP system, where is this wafer gonna go if I need to send the data downstream to an OSAT, who's gonna be testing the package test? So that connection to the ERP system is important for the financial team to be able to get real information and more predictability and better understanding on their economics. But it's also important for the engineering operations team in order to be able to add more automation, typically with the AI, in their production flows. So, that second opportunity is that SMH piece. We see quite a big opportunity for SMH. And then thirdly, you know, I said that a lot of customers were expanding their cloud offering. And one of the things that some of the customers have talked to us about as they begin this journey is the first thing they realized: you need to get all your data in one place. You need to be able to have that data aligned up and down the manufacturing supply chain, because if you want to apply ML models, you know, most of 80% of your time is often wrangling data and getting it put together. If you do that and with scripts and munging in the typical data science way, it's hard to then put it online, and it becomes very dependent on the engineer or person that built the model. If you have first orchestrated your data, sometimes people refer to this as a data lake or lake warehouse or different words for it, then you have a way of then building the AI and ML. We've had the expansion contracts that we talked about in this quarter, where customers really more and more relying on Exensio to provide more functionality there in terms of orchestrating and managing data. The MLOps product takes advantage of that, really, and it makes that much, much easier. And so it's like these three things that we see, I'd say, the majority of our customer base really working. One, okay, play another test. Two, okay, I need to orchestrate my engineering operations with my financial operations. And then three, in order that the foundation layer, if I can do any of that, I better have all my data for engineering and manufacturing organized in a central location with a common API, so I can build on any of these things. And those are really the three places where we see opportunity with customers. Great, thank you. And then as a follow-up, with respect to DFI, is it still the case that on the revenue generation side of the machines, that it tends to scale over time, that from the moment you ship a machine, the revenue generation starts out very small, and as the activity scales on that machine over time, it increases? Is that still the way you view it? Yeah, I think, look, I mean, one thing to keep in mind is our DFI engagements aren't just ever about just the tool itself. So it's a combination of software, and it's a combination of the hardware. And maybe even within the software, there's many pieces of software. Obviously, we've talked about the FIRE module, FIRE software, for example, which is used to inform our tool about the analytics that will be performed based on the design. And then there's obviously the other analytics software. So depending on the usage of those, some pieces may be accelerated. For example, if the machine is in the early stage, perhaps that one gets accelerated. We have talked about the lease treatment of the machine that can happen with some contracts. However, look, I mean, on the longer term, our goal always is that the customer's usage of the whole system grows over time. And that is why some of the past contracts that have been done have been on a token basis, such that we expect the customer to be utilizing, and hopefully we are providing them more value over the time, and therefore growing that opportunity. Great. Thank you. Thank you. Please stand by for our next question. Our next question comes from the line of Andrew Weiner with Samjo Management. Your line is open. Good afternoon. John- Hi, Andrew. You referenced. Hey, you referenced in the beginning of the call, you know, the, I guess, two lead DFI, I guess, you referred to them as customers, but I think, you know, one of those two is actually, it's a manufacturing evaluation, and. Mm-hmm. You know, they're not currently a DFI customer per se. But given what you described as sort of conversations about, you know, potentially deploying multiple additional machines, is it fair to say that, you know, that evaluation is going well, and that our sort of confidence level, that that will convert into a paying DFI customer has increased? Yeah, that would, that would be correct, Andrew. We shipped the machine at the end of last year, came up beginning of this year. Our second quarter was a very heavy usage period, as I said in my prepared remarks. We saw good, good results there. They did as well. You know, I think that really speaks to just the value the machine can create. We have a lot of hurdles we still have to get over in both customers. So we're not by any means, you know, able to just sit back here. There's a lot of work to be done, but we've gotten very positive feedback from that customer and yeah, our confidence is increasing. Okay. And then I just wanted to clarify, you know, when you said there's a couple of people asked about your capacity, and you've referenced a comment about not being able to support, and I realize, you know, these are, you know, conversations, not orders yet. But, you know. Right Sort of support what the two lead customers, I'll call the evaluation on a customer, would need. Is that meaning within sort of the current four to eight, that would be too little? And are you contemplating ways to be able to produce more than eight? You know, I'm just sort of trying to, you know, I, you know, I realize this isn't a revenue forecast or an order forecast, but I'm just trying to get a sense of sort of what the demand, you know, these couple of customers, you know, are talking about, and, you know, and what you're talking about from a perspective of what type of capacity you might wanna have in 2025. Yeah. So they've both given us ranges, and if it was on both from the low end of the range, then I think we would be okay. If they were on the middle of the range, we're probably a little short, and if on the higher the range, we're probably a bigger issue. And we're also mindful of the fact that we have customers that are asking for evals and potential demo machines, et c., that you know, as we see a number of interesting applications in memory and in other logic manufacturers. So we need to have some slack in that capacity. If we just did everything that was good for them, then maybe we don't have a way to expand our business in 2026. So we have to be somewhat thoughtful about, you know, kind of making sure we've got, ability to, expand out in the marketplace. So, you know, even under kind of their modest, the low end of their requirements, where I think we'd be pretty okay, we've also got to look a little bit at where we would be in terms of being able to do manufacturing, evaluations, et c. We, we've kind of also, you know, squeezed ourselves internally to support the three customers we're supporting right now, and so we don't really have much capacity as we would like necessarily internally for, demos with other customers and some other things right now. So we're a little bit hamstrung. So yes, we are looking to see what we could do to increase that number. We will make that decision as we get through this year to see how things, kind of build out with the customer base. You know, one of them could decide they don't wanna do it, right? So you never, you know, we've got to keep a lot of contingencies there. Sure. Has there been any conversations with the third customer that you haven't shipped the tool to yet, but I guess you're running wafers internally as to what their demand could look like or would look like? I mean, I know in the past you've talked about sort of most customers who are gonna be using it on any real vol-- you know, in any real volume, you know, would likely want at least a second tool just, you know, for redundancy purposes. We've not had very many conversations with them about that yet, Andrew. It is something we need to do as we kind of think about 2025 and beyond. So it will be something we'll do in the second half of this year. We've been pretty busy in this second quarter, just in early third quarter, just kind of understanding the first two. And then for some of those other applications you're talking about, you know, whether it's other logic players or memory, are they actually currently shipping you any sort of, you know, wafers so that you can, you know, run internally to demonstrate capabilities? Or is it more, you know, right now, sort of technical conversations? Yeah, we have at least one customer that I know off the top of my head that's already shipped us wafers in memory applications. We have others that are interested in doing that. We are mindful of bandwidth, right? So we're trying to kind of slot them in a way that we don't take our eye off the lead customers. But yes, we've already gotten memory wafers and starting to show them results. Okay. And then maybe shifting gears over to MLOps. I know that the first area of focus was, you know, the test application. Mm-hmm. And you had a number of pilots. Just maybe give us a little more color on sort of how you see... You know, are you taking on more pilots now, or are you waiting for more pilots to convert to, like, commercial, you know, engagements, and then you're gonna use that as sort of proof points to, you know, go out to other potential customers? And then maybe the third piece of that is, you know, I think you touched on the idea of getting all the data in one place and then being able to build, you know, machine learning or AI-based applications, you know, for which MLOps, you know, is the enabler. Is there any consideration to, you know, making efforts to put MLOps sort of in the hands of customers and let them sort of try to figure out how to best use it and develop their own use cases? Yeah, that's a great question, Andrew. So, yeah, to kind of go, if I can keep them all, you have a three-part question in my head. So, yes, there are other pilots, ongoing with customers and, parts of the industry. We are also taking a step back and looking and saying, "Okay, what, how could we make it better? What could we do that would make things more effective for customers?" And there's really two parts to MLOps, is the operations around orchestrating your data so you can build models, and then there's managing it once you're out in the field. The challenge was, the biggest challenge, not one of the biggest, one of the bigger challenges for that is in the test world, because customers use one company to do wafer sort test, a different company to do package test, a third company to do kind of a card-level test. Many of our fabless customers are effectively becoming kind of system companies, right? Because they're making entire cards or even systems at this point. So managing that data flow, once you have a model, monitoring the production, the test production results, and so they can put in rules when they wanna trigger a model update or change, et cetera. That's really the problem that MLOps solves for them, that, you know, both those problems. We think the second one is the stickier one over the long term, and we wanna look and take a step back and say, "Okay, how can we enhance that? How do we make that better for customers?" So there's an activity going on there. And then the third question you asked around putting it in the hands of the customer. The whole intention on MLOps is exactly that. They can use our environment and build their own models. Now, they can use the stuff that we provide, kind of default, you know, example models. They can use a different system. They could orchestrate the data in Exensio, pull it out from the APIs, use a different system, you know, from anybody to build their own models, and then publish that model back through MLOps throughout their manufacturing flow. So they don't even need to use our learning environment if they don't want to. And we designed with those three levels of flexibility in mind because the market really has all three of those types of engineers out there. They're the really early adopters that have built their own flows for doing model building, but they don't want to manage the day-to-day, 24/7, make sure the, you know, all the systems are up and you can get a model anywhere you need to, wherever there's a test showing up. That's not something, you know, you want to take expensive data scientists and spend your money on. You want software systems to manage that for you, and that's what MLOps does. are some customers that are, let's say, earlier in their journey, and they're happy to use our environment, but they want to build their own models because they've got know-how about their products, they've got experience with model building, et cetera. That's kind of like using a little bit more of the system. And the third case is, okay, PDF has an already defined pipeline for test time reduction or quality screening. Let me just tune that. Let me take that default model and tune it to my application. And those three capabilities exist in the product today. And that's something you'll see us enhance. If you were to have attended SEMICON at our booth, we had some smaller startup companies that are doing ML talk at our booth, because we are also looking at how we can work with the broader community. We're not trying to have the corner on modeling at all. We wanna help, you know, lots of people in the world bring models to production. And I guess maybe my last question is that, have we made any progress on any of the battery pilots? You know, you made that acquisition in that space, and. Yeah. So, we've been working with battery manufacturers and also battery consumers. Kicked off a pilot, or are kicking off a pilot this month. I think actually it's in another week or so. After, similar to what we did with the eProbe, customers sent us a sample material. We showed them in our lab what the image pipeline could do, how much information they could get about their battery cathode and anode. And now we're installing that software and system at a manufacturer. So both the manufacturer and the consumer of that battery can work together to identify production control. So that pilot will kick off. Now, it's an achievement that we got from the kind of stage where it's really just material in our lab, a very small amount of material, as you can imagine, in our lab, to a production environment where there's noise and there's, you know, it's not, there's vibration, and you're looking, you know, you're running meters per second of cathode and nano films and proving that the software can keep up with it at that level. So we're at, I think, at the next milestone right now. We're very excited about it. Okay, great. Thank you. Thank you.