Okay, let's get rolling. Now we're on to the Analyst portion today, with Adnan Raza, our CFO, and myself will be doing. Of course, you all know our legal obligations to point out to you. But the first part of the talk, if we did our job correctly this morning, is I'm just gonna bore the hell out of you, but at least more than normal. But I wanna touch on some of the high points we made earlier today, put it into perspective, a little bit of what it means for PDF. Obviously, Adnan's gonna come and talk about not only our progress from the last Analyst Day, but how we see ourselves going forward, kind of our next long-term view. So I think it's compulsory in our space, if you're doing an Analyst Day, you have to start with, it's gonna be a trillion-dollar industry, data's growing to zettabytes, etc., and this is all true. But I think in the context of PDF, why is this meaningful? Well, a lot of the growth going forward is being driven by AI, Automotive, the collection of more data, meaning more sensors, more high-performance computing. Both high-performance computing and mixed signal are core markets for PDF. We have a demonstrated not only customer base, but success in bringing value to the customers. So the areas of the growth line up with places where we've had experience and have had success in the past. Interesting, I didn't push the button. But this growth doesn't come without some headwinds we have to overcome. I think Sanjay spoke about it quite nicely this morning, as well as John, but the new architectures and materials mean that process flows are getting more complex. The maturity of the process is getting less, and that's not me doing that. The move to chiplets mean the supply chain is extending both in the importance and the complexity of the different stops along the line. But just the time from bare wafer into packaged chip outward, has a lot of needs for more rapid understanding while in line as to the causes of quality, reliability, excursion, more challenges for process control to have tight variation. And of course, we want the geographic disaggregation. Now, this industry has always been geographically distributed with fabless companies in the U.S., with the OSATs and the primary foundries in Taiwan and the rest of Asia. But we're getting to the point where we're combining more chiplets, combining more of the supply chain, and also standing up new capabilities in, you know, varied parts of the U.S., across Europe, etc.. This is gonna put a strain not only on the, the supply chain, but also on the engineering workspace, and the quality of the experienced engineers and the number of experienced engineers that exist. So the industry is gonna solve these, it always does, but these are challenges that are leading to inflection points that did not exist a few years ago. Of course, AI to the rescue. I don't know if, again, I've seen an analyst presentation in our space recently, didn't say they are an AI-Based Company, and AI was gonna bring a lot of capability. And AI may be inevitable, but it's certainly not going to be immediate. Massive changes rarely are, but in our space, AI, although a requirement and will bring significantly more value than it even does today, requires some challenges, again, to be solved. There's requirements. One, again, we've been talking about quite a bit today, is that need to combine that cross supply chain data integration, not just from a semantic model and the ability to integrate the data, but also establishing the network and the security for sharing that data to bring the higher level application. And it's not just about the amount of data. Our Industry, you know, my joke is: semiconductors was big data before big data was cool. We've always had a lot of data, but more data is not necessarily the answer. This is not an LLM type of space, where it's the sheer multitude of data solves your problems. It's having data that's informative of the problem, that the AI can drive the solution to root cause for you. Firstly, I'm impressed with the advances in models in machine learning and AI. Unlike some of you may recall, you know, about several years ago, I'd say, "Look, ML, yes, it's coming, but it's more like the pixie dust everyone's trying to sprinkle and cover the problems." I think today, given the cost to compute, the advances in the modeling, that it is today already having significant impact. But when we talk to our customers, it's less about needing help with deriving advanced models. The leading-edge companies, as John said, have groups of ML experts, a group of 20-30, but it's more about how do they go from the lab to the fab? How do they take these models and deploy them at scale? Even on a single model, you have to map from your ML expert through your production to all the different OSATs, if this is test-based ML, down to the specific testers. Now, take that and multiply it by the number of products you run. Mike O'Sullivan talked about 75,000 SKUs. Now, maybe you don't have to apply AI to 75,000 parts, but you certainly wanna get beyond the tail of just your two or three biggest high runners where your experts can approach. Based on that feedback, we've really kind of focused and doubled down on the operations part of the machine learning model to solve this challenge for the customers, along with the big data, along with the DEX network for data exchange, to really bring this to scale. Some of you have seen this. This all aligns with our Mission and Vision. We really want to be the world's leading data and Analytics platform for semiconductor and electronics ecosystems. We see this all integrating and being more interoperative as we go forward, not just in automotive, but in other markets as well. I think in many ways, it is consistent with the journey we've been on as a company for the last 30 years. When we started in the Integrated Yield Ramp business, it was about combining design and layout information with fab and process information to understand interaction. That has been expanded over time to test data, to more design information, to combining chiplets, to more assembly type of information, and we see it going upstream and in the third dimension to other organizations within a company. So we've talked a lot about the platform today, and again, this is the integration and how the development over the past 20 years has really grown together. I think there's three key things that are required. One is that AI drives to actionable insights. This is, how do you solve the workforce problem, but also how do you take the complexity of data to impact process control, capitalization, utilization, efficient use of your suppliers? And that's been a big addition on top of our Exensio and Cimetrix platform. No one owns all the data that they need to solve these problems. They don't even exist in any single company. So we felt for the last couple years that partnerships are super important as well. Both the ability to integrate, enterprise-level databases, but also to work with partners like the ones going next door, SAP and Advantest, to take not only their unique data, but their experience in their solutions, to bring more value by combining them with the breadth of data and capability that PDF provides. An advantage to us is that does bring us new go-to-market opportunities for our partnerships. The vast majority of them are significantly better, bigger companies than PDF. They have reach into different markets or different customer segments. And of course, the transition to automotive electrification is a growth vector to us. You know, we'll talk about not only silicon carbide, not only the mixed signal companies that are on pretty significant growth vectors, but adjacent markets. And in PDF, we've really been applying AI and really machine learning across a number of our internal products. This is really being driven to bring solutions, to bring actionable, insights to our customers versus just analysis. But also to drive that total experience so that they can get their typical engineer to be as effective as their best engineers. That's been feedback to us from a number of our customers as to why they believe that, platforms such as PDF and applying things like ML can be most effective. Yes, it's the aggregation of data. Yes, it's solving the complexity, but it's also bringing this, capability to a broader part of their, of their customer base. We've seen this transition, at a slower rate in the past, where more and more people consume the results coming out of Exensio in our platforms, versus having to generate it at the expert level. In fact, our business model, being mostly data-based or tool connection-connectivity-based, is driven by the value being brought from the data and not the number of users. AI is just taking this to the next step. But it goes just beyond our products or our solutions, but our customer solutions across, you know, the equipment space, the design space, the ERP space, even to the cloud Analytics, that their solutions and capability combined with PDF bring even more advanced capability to the industry. And I think the work, again, that they're talking about next door with SAP and Advantest, I think, are highlighting that. That no one company can bring the cumulation of the data and the expertise necessary, but built on the PDF platform, it brings the scale and the ability to reach different parts of the customer base. That doesn't want to go forward? Okay, let me back up. So in addition to the data or the applications that customers bring to us, I think the strategic partners bring specific benefits to PDF on the business side. One is it enables new products. We have new capabilities, bring more value, not just from our core data, but bringing it to new users. SAP is an excellent example, where we can leverage our engineering data... with the financial data to provide more detailed solutions, such as product costing, RMA analysis, cost of yield, to an entirely different group within our customer base. That makes PDF not only more sticky, but brings more value to the system. As I mentioned, customers from partners such as IBM, Advantest, SAP, Siemens, are not only significantly bigger than PDF, but they also reach a different customer cohort within our customer base. And so this really is going to greatly help us in our go-to-market capability by leveraging their channels. And concurrently, we also, in several cases, have been able to introduce them to executives on the engineering side. So we're pretty happy of where we can leverage not only these enterprise platform partners, but also the partnerships on the equipment side, in the engineering platform. So in addition to machine learning and LLM Models and growth in high-performance computing, automotive electrification is a big push. You know, whether the U.S. goes to completely electric cars by 2035 or 2030, remains to be seen. But the fact of the matter is, the use of more compute within cars, the use of more sensors, is an inexorable path that we've been on and will only continue. You know, for us, in addition to this breadth of data, the traceability, the storage required across that Supply Chain, leading to more use of Exensio, more use of PDF as a platform, there's other specific growth areas, for example, Silicon Carbide, driven by Power Electronics, where the complexity may not exist on the product level, but certainly exists on the process level, from the boules through the fabrication. We have business in the space. We have people reaching out to us with more interest. We see this as, again, one of the areas in our space that plays to PDF's strength into understanding parametric data and complex analysis, combining both process information and the end characteristics of the product you care about. You know, lastly, we announced in the previous several months, the acquisition of Lantern Machinery Analytics. And the reason we did that is we had a reach out from several, you know, customers or potential customers on how can they solve the manufacturing challenges in the battery space. This is a market where, as they told us, although fewer steps than a semiconductor flow, has a great deal of complexity and variability, where the data analysis necessary not only to understand and diagnose the causes of quality or quote-unquote, "yield failures," but new methodologies on how to control it in mass production. Again, we believe this is a combination of having the right data and the right analysis. Through working through some of these pilots, we are pleased to see that not only is the Exensio Platform and PDF's capability very applicable, but the opportunity to bring this differentiated data, again, one of the reasons we did the machinery Analytics acquisitions, one of the reasons we partnered with Voltaiq, is to bring to Semiconductor the capabilities, and to us, the business opportunities that we've seen in semiconductors. So a slide that you've seen before. We really view PDF today as an Integrated Platform. And as Saeed mentioned in the previous slide, that data access and integration, both through our Cimetrix Data Connectivity, through our ability through standards, and to hook into literally thousands of different equipment models from our DEX network to combine data across the Supply Chain, is extremely valuable. But PDF is very unique in its ability also to augment industry data with that additional data required to have insights into root cause, into process control, to really have high profitable mass production in the future. I think Sanjay summarized in his talk this morning both the challenges and ways that PDF brings value to our customers in helping to achieve these problems. So when we did our last Analyst Day, you know, we were just crossing, had just crossed Analytics being a dominant portion of our revenue. You know, today, Analytics is the dominant focus today. Although Integrated Yield Ramp may still exist, recall that it's mostly a business model standpoint and how that we can help our customers achieve value and capture a fair portion of that, more than a technology play at these days. But the key is bridging that supply chain, providing solutions that drive value in mass production, not just technology development. So from PDF, the expansion is not just with the growth of the industry, and via the partnerships, but from a customer base standpoint, we've continuously seen an expansion of the potential customers. You know, those of you who've known us for years, particularly when we were dominantly the Integrated Yield Ramp company, our customer base was foundries, IDMs. And even within that space, it was when they are doing ramps of new technologies. So it was limited both in scope and time for interaction or gaining business with them. But since that time, we've grown not just from yield ramp, but into mass production, not just from the fast followers, but really across the fab and IDM business, not just on leading-edge SoC, but quite a bit of business in mixed signal and other markets. We've grown into having a much more appreciable business with the fabless, as they own more of the supply chain responsibility, particularly the cost of test, the risk of OSAT assembly, and time to market. With the Cimetrix acquisition, and some of our partnerships, the growth on the equipment side, has been significant and important to us, and we're seeing more, albeit at a lower level, business on the electronic systems manufacturers, but also at the system level. Most of our business today on the system level are the companies that have chip arms, but even they are starting to talk about how chip capability, chip yields, managing the supply chain, affect their overall product challenges, not just at the chip level. So we're pretty confident today, or I should say, comfortable, in the positioning of PDF going forward, right? I think, you know, John made the comment to me, not that long in the past, I totally concur, that we think PDF is more well-positioned for the opportunity in front of us than I think we have been at any point in the past of our 30-year journey. And with that, I will turn it over to Adnan. This has a mind of its own. How is this now? Can you hear me? Now? You can hear me. Okay, perfect. So, again, very nice to see a lot of familiar faces. For those on the phone or listening into the webcast, Adnan Raza, CFO at PDF Solutions. To start off, I'll make an admission. I have not been here 30 years, unlike John and Kimon, who you met, and obviously, you've heard about. Neither have I gone to Carnegie Mellon, although I did share the state of Pennsylvania at the other end to go to school and met with some fellow alums today. I will say, sometimes I wonder if I said it really fast, that my younger brother went to Carnegie Mellon, and maybe that's the piece of it that they found had some affinity with, and here I am. That said, also for those listening into the webcast, I wish you were here and able to hear some of the talks that we had from outside speakers. I think since you weren't able to hear them, I'll just mention it that, you know, we're very thankful for the gracious, nice words that folks like ADI and Intel and GF had to say about the capabilities of PDF and more so about the trust that we bring to the relationship. We sincerely believe we are the end-to-end market player in the semiconductor Analytics space, and have been here for a while and have been developing those solutions, sometimes taking longer, up to 10 years like John mentioned in his presentation. The next section, market size and opportunities. Thank you for advancing the slides. I intentionally put this down. Look, I mean, we were debating whether to put this slide or not, so let me give you a little bit of context. What we did was we worked with a Wall Street investment bank to look at three categories of companies, as noted on the right side: the fabless logic, the IDM, and the memory and the storage categories. For each of them, we looked at what are their disclosed R&D spends and what are their disclosed cost of sales spend. Then the investment bank also had conversations with the IDCs and Gartners of the world, some of the analysts that you heard throughout the day, to say, in their estimations, there is no clear report out. So in their estimations, what is the percentage of those R&D numbers or cost of sales numbers that are spent by those companies within the, quote, "Analytics bucket"? We did do a triangulation to compare it versus the fab, versus the EDA spend, and, you know, got to a point where we felt comfortable enough to share that, look, A, in my mind, the takeaways are, A, that the industry itself, from 2019 to 2023, when you look at it, it's about a 40% growth. Why is that important? Just looking at the metrics this way, it will be apparent when we talk to the next page, but just keep that 40% in mind over the last four years. 2019 was our last Analyst Day, 2023, obviously, where we are. The second thing to think about is, you know, whether the numbers are big or how big and whether you believe it or not, or whether you find yourself more in Marc's camp this morning, who astounded me with a number of $7 trillion for the AI opportunity, which was great to hear. I think the key thing to note is, as you look forward from now till the end of the decade, you're looking at just under 10% growth rate for the industry. So nothing, you know, short of saying, "Okay, great, good growth ahead of it," that the industry has to serve. You go to the next slide. This is a more PDF style, honest, bottoms-up, SAM-style assessment that you have seen us talk about. In terms of what are the areas where we think we can provide our product, what was that opportunity? How big is the market where we can actually sell products into? If you go back and look at the 2019 deck, it was $1.3 billion. We looked at it today and did a full bottoms-up analysis, where we looked at all the individual players in the spaces, the different products that people serve, what we know about those industries, what are the estimated market sizes of those opportunities, and we came up with $2.6 billion. There is a very small AI-Driven plus factor that we incorporated into this, but nothing like the pixie dust extrapolated 7 trillion-ish kind of numbers, obviously. But it comes out to the $2.6 billion. When we sat back and then started looking at this data and put it in the context what we talked about in the prior slide, where 2019 to 2023 today was a 40% growth for the industry. We felt pretty good that the market size today is about two times what we felt it was in 2019. Second thing, if you go back and correlate it versus the total growth rate that we have demonstrated over this time period, PDF, you know, is about double the size, just under double the size compared to where we were. I think it's here also important to note that, look, PDF is a company that has invested in a lot of technologies. You've heard us talk about, you know, DFI. You've heard us talk a lot about AI and ML, but 2020 onwards is where we felt the momentum of our activity started to come through. 2020 and prior years, a few years before that, we were flat in revenue, candidly, if you go back and look at the numbers, and most of you know. However, 2020, our bookings were quite strong, well over 100%. We've talked about this. Some of those results of those deals then started to come into 2021, where we demonstrated and clocked in 26% growth. All of you remember the Cimetrix acquisition, which was done the year prior. So the conversation also was that in 2021, the growth rate partially came from acquisition. Admit it, absolutely happy with that acquisition, and by the way, it's performed better than we expected, particularly when the CapEx cycles were strong. 2022, therefore, was very important for us to clock in 34% growth, which we did, which was exceeding the prior year with the acquisition. So overall, we felt, you know, we were demonstrating good results. That's the kind of second bullet on the overall revenue growth rate. But then if you go and look just into the Analytics revenue growth rates and think about, okay, how has Analytics done over time? And you all know that we were transitioning from a historical gain share model to an Analytics Model. This is where the remarkable number comes in, 2.8. For those of you good at math, it's 35% CAGR. I'll put it down on another slide later. We'll talk about that as well. Why has all this happened? In my mind, it's a perfect storm. It's where our cloud technologies have become mature. We are starting to make good inroads with multiple products, and I'll share with you some of that data a little section of the slide into the customers, and where some of our ML and advanced solutions are making headway into the customers. And finally, what you all have noted as our spend in the DFI category, for many, many years that we've been doing, and, you know, a few of you have rightly complained to us. But, you know, that has started to bear fruit. If you actually go back and look at our Q1 2021 earnings call, I think that's where, you know, kudos to Johnny, came out and said that this is the year either we'll make some, you know, dollars out of this investment, or we'll think otherwise. And pretty glad how things turned out after that in terms of we were able to make the progress, and for a long, long time, we weren't able to say which customer we're working with. But thanks to the presentation from Intel today, and appreciate their gracious acknowledgment of our activity and help that we've been doing there. So it's a combination of those factors, which we're very pleased about the overall growth and the transition that we have done and also the Analytics. Now, I'll put this in another context. I talked about 2021 growth of 26% and 2022 growth of 34%. This year, obviously, just like we put out in the press release, no different than what we talked to you about at the end of Q2. This year, we're targeting lower double-digit annual revenue growth rate, which in of itself, compared to the industry, is still pretty good. And also, when you look at it over the last three years, it's still north of 20%. So our long term, from a growth perspective, as a Software company, creating new products and 20% delivering growth over a multiyear basis, average, is something we feel pretty good about. Let's see if I'm gonna compete with you for the next slide. No. All right, out of control. All right, so this is a section where I wanted to talk to you a little bit about progress into the last analyst day. But as I thought about presenting this section, a lot of our conversations started coming back to me in terms of how to explain PDF. A lot of you keep coming back and saying it's a complicated story, hard to understand, hard to explain, and I see some smiles in the audience. So, you know, for me, I had to make myself a little reference sheet of, okay, what are the different pieces that go into PDF? What are those different product offerings? How do we structure the revenue for those different pieces? So let me just give you a walkthrough, and then that'll help frame some of the thinking. All right, so at the highest level, if you go to our filings, everybody's familiar with this level of disclosure that we do. In terms of revenue, we break it out into Analytics and IYR. We've also talked about how Analytics, again, many times today, has become just around ±90% of our revenue, depending on how you look at it or which time period. Further description, within the Analytics piece itself, let's focus on that one. There's the Exensio piece, which is the core software piece that we have developed. Many ways to deliver that. I'll talk to you about in a second about those. And then there's the differentiated data. What is differentiated data? Differentiated data are the different ways in which, like Saeed mentioned, when we cannot find the data available in the system, we go and create that data. What are some of the examples of that? This is the DFI machines that you hear us talk about. This is the CV infrastructure that you hear us talk about, wherein we use the knowledge of the FIRE software combined with those machines to create the differentiated data, with the help of which we can use the unique Analytics that we provide to our customers. The third piece within the Analytics bucket, of course, is the, L et's go back, is the Cimetrix connectivity, which is the acquisition that we believe, that we did. We feel, I mean, if you think about the platform picture that Kim drew earlier, it starts with having data generation, then it starts with having the connectivity for that generation of data, and then the Analytics engine that you build on top of it, which is what makes for a good platform. If you go back and look at our platform slides, this deck is posted, obviously, all of that has to be encompassed with a trust that encompasses the entire span of that value chain on one side. And the other side, we talk about the partnership that you've heard throughout the day, including the SAP presentation that's going on.... The bottom portion is obviously the Integrated Yield Ramp, wherein we go in and we'll charge a fixed fee and then collect the Gain Share, some of which Gain Share contracts, even today that we have, that we're reporting go out till the end of the decade. A little bit more description off that I just described for you. Welcome to read it later. But then I think the important piece after that is to think about what are the different types of software or what are the different types of structures within each of these different categories? So if you stay with me on the Analytics row, within that, the Exensio Analytics, and just kind of follow it across to this last column, you'll see it under the type, the kind of first three pieces. The way we sell our Exensio Analytics software, like a lot of software companies do, is, is three ways: perpetual customers to perpetual software. A lot of our customers in this industry, you heard the talk from ADI about the history of integration, of acquisitions. You heard about it's been done a certain way. A lot of our customers will prefer still to use the software perpetual basis on-premises, which is we are happy to sell, which is how we are happy to sell it to them. Some customers will get it from us on a term-based license basis, which is again, installed on their premises itself. It gets a little bit complicated. Some of the customers might come back and say, "Oh, we'll do a term-based license, but we're doing on cloud." But keep it simple. It's perpetual, and it's a term-based license. The third piece of that is, of course, the cloud piece. So when we're talking to you quarter to quarter on the earnings call, then we talk about different deals that we have won or what kind of nature of those deals. This is the framework that at least I found handy for myself that I wanted to share with you. That's how it fits in. The others start to get a little bit simpler. In terms of differentiated data, how do we charge for that data? It's term-based license. You heard us talk about some of the large enterprise deals we've done. In the Cimetrix Connectivity space, it's mostly perpetual software. We're starting to just dabble into looking into if we can make some of that recurring, but it's way too early, but mostly all perpetual. And again, in the fixed fee and the Gain Share side of the things, we have talked about how it's service and time as well as royalty payment. This page is important. Also, when you think about the backlog number, the remaining performance obligations that we talk about and disclose in our 10-Q filings. What we disclose is our committed, confirmed, non-cancelable orders. That is what is our backlog number. What it does not include is when customers will come in and buy some of the software on a perpetual basis. Because again, we don't know when that order is gonna come in. When it comes in, it gets fulfilled, so it's understating that. It also doesn't include some of the... the... And perpetual software, by the way, both in the Exensio piece as well as in the Cimetrix Connectivity piece. Then it also does not include some of the Gain Share pieces in the backlog portion, because Gain Share is a function of the end customer's production capacity, and we don't know what that is going to be. We're obviously happy to collect that check when that production happens and comes through, and it's, you know, very high margin for us, so we're pretty pleased with that. So hopefully, hopefully, this serves as a good kind of cheat sheet presentation page for the rest of you. Thank you for advancing. This is a high-level summary. So from here, you know, I'll present to you a few data slides, but, you know, instead of just going slide by slide, let me just give you a little bit of a roadmap and kind of road signs to watch out for along the way so you know where we are in the presentation. So we'll do nine slides. Think of it in, like, three sections. The first three will just give you some data, some observations. The next three will be some insights that I will share, that underscore some of that data. And the last three are going to be just zooming back out at the corporate level for what that means for the entire company. So let's go. This is kind of, call it slide 1 A, if you will. Let's stay back to that one. I think I've covered a lot of it, but just to highlight some of the things, 20%, even on a CAGR basis for the total company, which is the point that I was making, that even with the slower growth that we had talked about for this year and shared with you all, it's on a CAGR basis, is 20%, measured over the TTM period compared to when we talked at the Analyst Day. And then again, the CAGR, 35% that I alluded to earlier, a few slides ago as well. Analytics now, last bullet on the right is also important to note. It's right around 87%. It'll fluctuate depending on what kinds of deals, when we're doing them, what is the structure? Sometimes the gain share payment may be high unexpectedly, sometimes the gain share payment may be low unexpectedly. But depending on that fluctuation is where that number will, plus or minus oscillate, but I think it's right around 90% is a good yardstick to think about. And that'll be important when we talk in the last section about why, we're, Y ou know, we think of Analytics as a driver for the whole company, and that's how we'll share some of the, long-term targets that we will discuss. Next slide. Okay, slide number, call it 1B, if you will, in that kind of three-part structure. Look, we're pretty proud of how the business model has transitioned and how our margins have expanded over this time. Of course, we're also proud at the same time of the balance sheet, which we're not talking about here, continuing to maintain a healthy level of cash, not having any debt. But put that aside, as the business has transitioned to a recurring nature, as we've been able to better utilize our resources and people, as we've looked at how do we take benefit of the scale and put our people on projects where we can demand more from the customers, it has started to show itself in a higher gross margin. It has started to show itself in a higher EBIT margin. Gross margin, you can see 900 basis points improvement, 74% as measured over the TTM period. Last couple of quarters, it was right around 75, and then again, on the EBIT margin side, right around 19%. Put this in the context of when we last at the Analyst Day in fiscal 2019, our gross margin target at that time was right around 70%. So we've been in excess of that. EBIT margin, we're getting pretty close to approaching that, which is a 20% target that we had set at that time. Again, we'll talk about new targets. So kind of my third next slide, my third and last piece from the observation piece, this is new data. A lot of you have been hearing us talk about recurring revenue, and a lot of you have been asking us: "Okay, can you give us some sense of how much it is? What's the measure of it? How should we think about it in terms of scale within the Analytics or within the company itself?"... We went back and looked at what that number meant for Q4 of 2019, and then also on a trailing twelve-month basis, which we think is the best measure, because quarter to quarter, you could have some fluctuations depending on accounting and depending on Rev Rec rules. On a TTM basis, it's approaching right around 70%. Again, a number we're pretty pleased about. If you think back to the earlier slides, where I talked about there is a part of our business that is perpetual, there's a part of our business that is nonrecurring, if you will, that we also don't include in the backlog. There is always going to be a portion of our total Analytics revenue that is going to stay either as perpetual or it's going to be, you know, as the customer is just buying the software, you know, so that it is not of a recurring nature. All of those will mean that there's a terminal limit, and also there are some services. So all of these things mean that there's a terminal limit. This probably doesn't get to 100%, but it's a number of 70% is something that we're very comfortable with, and I think it's a pretty good metric as well. Okay, now I'm gonna start showing you kind of the second set of the 3 slides, which is some of the data underlying that. This particular data is about the Analytics revenue per customer. If you look at our quarterly investor decks, actually, we put this as a bullet on the slide. Some of you have noticed it. Some of you actually, A students in this room, came to me just 10 minutes before this presentation and told me of the slides that were already posted. They had studied those, and they had one or two questions, so kudos to you guys and call you out a little bit. So in terms of Analytics, if you just look at the revenue per customer, pretty pleased with the fact that it is just under double, you know, from $455,000 to $840,000. We are, as noted, not including some metrics. In this metric, some metrics tends to be anywhere from a few hundred dollars to a couple of thousand dollars, but it's very different. It's serving a very different end of the market. It's more connectivity software that's going on the tool when it's gonna get shipped. A little bit different than what we do. Fits very tightly from a strategy standpoint and a platform standpoint. But when we think about Analytics revenue per customer, we think xCCG is the right metric. And like I said, pretty pleased with the fact that it's just under double. Why does it happen? It's all of those things that we've been talking about, which has been the theme throughout the day. It's the platform. It's the transition to the cloud. It's the recurring nature of the software. It's more stickiness. It's the ability to sell a larger platform of our - a larger portion of our platform to the customers, and I'll explain that in a slide or two as well. Next slide. Okay, so, I think we, in the last Analytics that we did in fiscal 2019, we talked around some of these numbers, and I think we may have had a slide or two at a higher level, but just wanted to be a little bit more explicit here, this time sharing, since we currently have a little bit more data and a history of having served the customers. So if you look within Analytics, and then think within the Exensio software itself, then within that, think about the four modules that kind of mainly constitute the Exensio Platform. It's those four modules listed on the bottom right, the Manufacturing Analytics, process control, test operations, and assembly operations. Then we went ahead and we said, "Okay, let's look at the average revenue by the number of modules for these customers and then see how does that compare?" And, you know, typically, when you add another module, the point of this slide is to say, show that it's not just the doubling of the revenue that we tend to see in our customers. The power of the platform is such that we are able to see just under 5x the revenue that we're able to make with one module when we go to a customer using two modules. Beyond three, we're a little bit in the early stages. There are customers that are using those, but we are able to see a step up of about nine times compared to the customers that are using the one time. As customers adopt the platform, we think this is one of the areas where opportunity lies to expand our presence and become more and more relevant and sticky with the customers and grow our revenue. Next slide. On the last slide, I alluded to there are a few customers, so, we put a bullet point on the top right. It's worth, taking home. Over 20% of Exensio module customers today are utilizing two or more, so it's just about 20%. That should give you an idea of how much opportunity there are, in our customer base of those that could use, two or more modules. That also gives you a sense of the fact that, yes, we have demonstrated this growth of CAGR of 20% and CAGR well north of 30% for our Analytics customers over the last few years. But even now, we're just about 20% mark for the Exensio customers using multiple modules. Within that, it's a little bit classic PDF-style title. You'll probably have to read it three times to understand the exact math behind it, but, if we looked at the average of the multi-module customers' revenue, and if you add them up for any period, then we looked at, okay, over that period, for example, the trailing twelve months of Q2 versus the entire calendar of 2019. So using the same methodology to compare, what is the growth in those spends, in the spend amount for those customers? And again, it's a trend that we see. Average revenue per customer that we saw for Analytics is increasing. Average revenue, when the customers are switching to multiple modules is increasing. And then this in and of itself, what this is saying is customers using multiple modules are spending a lot more today than they were a few years ago. But all the different pieces together is kind of where I think the story comes true for PDF and why we're feeling like we're at a great spot, given all the trends that Kim also alluded to earlier in his presentation. Next slide. Okay, so this is, this is the last kind of the three-part sections, if you will. So kind of done the six data slides, now we'll do the last three data slides. If you looked at us compared to 2019 versus now, again, zooming back out at the corporate level, 6%—when you look at combined revenue growth plus the Non-GAAP EBIT margins, about 6% the last time, and we had the discussion with you guys for the full calendar year of 2019. I guess 2019 wasn't over then, but this is full year 2019 measure. But today, when you look at it for a trailing twelve months of the Q2 that we've reported, it's sitting at right around 47%. How do we feel about it? A, we feel pretty good about it, but as we think about it, you know, what is our North Star? How do we think about the opportunity? There will be periods of growth. There will be periods where we'll be able to charge higher margins. But, for us, when we think about revenue growth plus an EBIT margin, we think of 40% as a good yardstick to think about. Again, this is our long-term target. There will be periods in which we may not meet that, right? Where we're investing for growth ahead of it coming in, and we're, you know, spending a little bit more on the operating expense side than we might wanna, than it might be really apparent. I mean, if you go back and think about the time when we did the Advantest deal, back then, our spend in the cloud was well ahead of the opportunity itself. Again, we were getting ready for a large opportunity, in that case, a $50 million deal. If you think about some of the leading edge engagements that we did in 2021, we were, again, spending ahead of those opportunities, which is why our operating margin takes a little bit of time sometimes to come through. But this will remain sort of a good yardstick that we will continue to think by about ourselves. Then the second of the last section of 3 slides, another new data point. Some of you have asked us, "Yes, we see the disclosed number of the total backlog number in the filings, but how do I get comfort," some of those people that asked the question in this audience, "How do I get comfort around how much of that is going to convert into revenue for the next year?" Right? So, a low number in this case is a good number. Because a low number means that my total backlog, in and of itself, is pretty large. However, the amount that I'm gonna convert into next year, as a percentage of what I will eat out of that, what number is that? It's not a hard rule per se, but, you know, we think somewhere around 50%. Does it oscillate around that? I don't know. Maybe we'll see. 45-50, somewhere around that range. We'll see if that number kind of oscillates around the 50% mark. Today, looking back, at Q2, it's at around 44%. And when you go back and translate that into the dollars, you'll see why we start to feel comfortable when we think about growth rates, why we think comfortable when how much of the revenue is spoken for next year. You know, candidly, since you know, I've joined about three and a half years ago now, whenever we've done the AOP for the next year to our board of directors, we've included this metric about what percentage of next year's revenue is already in the bag. So this is another way that we track this internally, that today we wanted to share with you. Lastly, turning a little bit to the balance sheet. I alluded to earlier, Kim and John are very conservative folks, so debt has not been on our books for a long, long time. And we intend to keep it that way, unless something really amazingly in terms of opportunity presents itself, then we will revisit it. Not to say that it's cardinal to never to look at that. Today, you know, last quarter, pretty healthy cash balance, as well on our balance sheets. But then if you go back and look from 2020 through the last quarter, how much have we spent? It's really been in a lot of what we think, good ways to return the capital to the shareholders or invest for the future, you know, stickiness and sustainability of the company. CapEx are around $25 million, so I'll call it about $25 million-$27 million for both CapEx and share repurchase, and the balance of about $30 million for M&A. The predominant portion of it, you know, the older portion of it was, our, Cimetrix deal. Pretty pleased about the balance sheet that we've built, pretty pleased about the margin progression that we have done, pretty pleased about where we sit with our opportunities, and also the underlying metrics that we're today giving you a little bit look under the covers for. Next slide. Okay, this is the last section, opportunities ahead and kind of how we think about them. If you give me the next slide. So long-term target financial model, we went back and we looked at what we talked about at the last Analyst Day, 2019, and you'll see that, you know, we put two rows there for the revenue growth. The last time we were talking to you, we were talking about the Analytics revenue growth. Now, with the business having transitioned over to mostly being Analytics, it makes sense to talk to you about the total revenue growth, because essentially that's a proxy for the Analytics growth as well. We think, you know, given the successes that we've seen, the stickiness of our platforms, we're seeing the business over the long term. As we think about our revenue, as we think about our total growth, we feel like greater than 20% is the right target to set over the long-term basis for our total revenue. On the gross margin side, last Analyst Day, we had talked about 70%. We have started to clock in 75%. Quarter to quarter, again, there might be variations, but our North Star of what we want the long-term gross margin target to be is going to be 75%+. You know, we think there's a path to get there, as we have demonstrated the last few quarters. On the Non-GAAP operating margin side, if you just go back to the last slide, we've stated the 20%. We're not changing that. You know, the reason we're not changing that is because of the variety of opportunities that you're hearing us all excited about today. For us, it's always going to be a balance between what revenue growth do we deliver versus what operating margin. A few slides ago, we talked about roughly 40%, the sum of the revenue growth and the EBIT margin, the way we think about it. However, on the operating margin side, we need to be able to continue to invest. Some quarters, it could be higher than that if the opportunities are, you know, at the different times of investments, and some quarters may be plus or minus that. But, I'm pretty excited about the fact that we can talk to you about total company growth, and then the combination of those two as we look at it, being 40%, as our North Star, if you will. Some of the areas that we're looking to invest, and candidly, these are not modeled into our internal projections right now, but we're pretty excited about. You know, one investor called, as we were getting ready for this analyst day and said: "You know, one thing that would be exciting to hear about from you guys is, uh, DFI Pro adoption is starting to happen, but it's still in the early stages. So what is the free option value? What is the option value, essentially, a shareholder gets in buying a PDF stock? What are the opportunities that could, you know, become big over time?" And candidly, as we sat back and thought about it, these are the four that came to our mind.... So DFI eProbe, like we discussed and, like Intel also talked about today, starting to see early signs of adoption. Pretty pleased with that. Obviously, as that scales, if there's, you know, more opportunities with that customer or more opportunities with some of the other leading-edge customers, some of the analysts have talked about how big that opportunity could be. Some analysts were in this room, actually, I see. Premium, again, an opportunity that has started to have early proof points. We started with this in China, and it was, they always say, necessity is the mother of invention. But, candidly, the inbounds that we're getting for the number of customers was a lot, and we couldn't serve all those customers. So we started offering a limited amount of database, limited amount of capability, things that people could do, and seeing who was using how much of the platform, who was going back and constantly deleting that space to reuse that. Well, there you go. That's my free marketing plan or free sales plan to figure out who is the customer that I should go and market to them. We were surprised how many of those we were able to convert into actual paying customers. In some of the discussions we've talked about, you know, what those numbers are, but pretty, pretty surprised, higher than what we would have expected going into it. Partnerships, I think you heard us talk a lot about as well. When we think of partnerships, we think of them across the whole platform. So it is on the equipment side, it is on the test side of the Siemens of the world, it is on the SAP side, going vertical to the platform itself. And it's that ecosystem for us that makes for a sticky platform, and that's why we become relevant as well as we establish more of those. Battery, early stages. Obviously, some of the few touch points we've talked about here are the machinery Analytics acquisition that we did. It's basically a camera system, not too dissimilar than some of the other data observation systems, if I can make it at that high level, like the machines that we have in DFI and CE. It goes in, and it's able to analyze some of the battery characteristics during the manufacturing process, to say, "Okay, we're running some machine algorithms on top of it to predict what the yields could be." Candidly, this is not an opportunity we were such forthright about in going out and seeking. As the battery industry was developing these batteries, and the three major players in the world in this market that kind of have the majority share, close to 70%, I think, but check my numbers. One of those guys reached out to us and, you know, talked to us about the process yield improvement and which other areas they had seen software tools used for, process improvement, semiconductor being one of them, and their PDF was a name that everyone talked about. So it was an inbound discussion that came in, and we did a pilot, along with Lantern Machinery Analytics and found that to be a pretty interesting solution, which is why we went ahead. But it's a combination of these opportunities. I mean, if all of these come through in a meaningful way and start adding to revenue, there's sprinkles and starts of some of these happening already, then I think we're pretty well positioned, which is why we're excited, and which is why we think about expanding some of the dollars along the way into making sure those opportunities are invested in the right way before they start to bear fruit. And then I think this is the last slide. We kept this one. This is similar to the one that we had in the 2019 Analyst Day. We went back and looked at this. Pretty proud about all of the things here. You know, these are the things probably were more promised, if you thought about it our last Analyst Day, in terms of improved profitability, increased visibility. Through the last few years, hopefully, we have done that by improving our margins and hopefully by giving you some more data, starting to talk a little bit more about the backlog that we have been for the last three quarters. We have started to deliver on some of these metrics as well. As Kim is checking his watch, if we go back and go to the next slide, I think that is it. With that, we will thank you. You know, obviously, other sessions are going on. We're happy to take a few questions. Kim and I are both here, but to the extent anybody wanted to hop back into the executive sessions, you always have that opportunity as well. I think they're going on till 5:30 P.M. or so, but with that, we'll open up the floor. Any questions? Happy to discuss, happy to answer. So, Kim, we started to talk earlier, like if the DFI opportunity continues to expand, at what point do you have to decide the model? Is it a big CapEx commitment for you? Do you have to partner? You know, what are the practical considerations in terms of how you could scale that? Yeah, it's a good question. I think it's obviously a good problem to have. And I think from PDF, you know, our perspective is we're gonna do what's the most efficient from a go-to-market, from a shareholder standpoint. Have the ability to go between scaling internally as we do today, to using third parties on the manufacturing, which is what most of the CapEx guys do. So if it makes sense to partner or outsource the CapEx portion of the DFI solution, that's a possibility. I think what's unique in PDF is, or in the DirectScan and the DFI solution, is there's a recognition in our customer base, the software wrap around the differentiated tool. So as a company that's Analytics-focused, we don't have to make all the money on the tool. So right now, the customers are more than happy to stay on the subscription model of the tool. If it per se works out that we're talking to our customers, moving the tool to a CapEx via PDF or someone else, we can kind of position in the sense that a lot of the value is in still providing the software on the tool setup and the tool analysis, which I think our customers have understood. ... If If you have a transistor with or a layout of 50 billion, you know, transistors and hundreds of billions of structures, understanding how to drive a recipe and what it means to analyzing billions of measurements that come off of it is a big Analytics problem, regardless of the tool. We've already solved that, and I think, that is going to be a continuing point regardless of where we move on the CapEx side, be it from a balance sheet, management standpoint. Parts, etc., that will maintain, I think will, will maintain a lot of the value. Any other questions? David. Well, just gonna get it to Mike, because we wouldn't know. When you think about the opportunity for DFI, years ago, you talked about, you know, the number of tools that you could potentially place. Is there something you could provide along those lines now for how you're thinking about the opportunity going forward, either in the intermediate or longer term? Yes, I think, you know, there's one way to look at it is kind of a minimum bottom, bottom line. If the key for PDF and the reason we got into the eProbe business, is we believed there was a need for mass production, not just technology. In mass production, by definition, no one is going to have one of a kind, because there's always maintenance, there's always downtime, etc.. Although, by the way, our feedback... Stand up straight or talk louder? Okay, I guess everyone can hear. Is, and by the way, the uptime on our tool, I think, is better than anyone in the industry right now. It's minimum two tools to one, right? Per facility, and that's assuming minimal sampling today, from that standpoint. So it's tens of tools to dozens of tools out in the field over a very short period of time as you move into mass production. If you get to the point of you need high sampling, you need multiple measurement points in a fab, not only for the front end, but for, say, the 1X metals, the complexity in the back end, where Sanjay pointed out today, maybe even on the backside power, then you have to look and say, what fraction of the entire inspection market moves from optical to this? And then you can look at, you know, KLA's sales and market cap as a, as a metric, and take some haircut of, you know, 30% of it going forward is, is an upper end, maybe possible. So it's a significant market, which is one of the reasons we saw the need, but we also saw the potential opportunity, which is one of the reasons we have it. Thanks, for the presentation today. Very helpful. I have a couple. First of all, can you just clarify on the DFI side, is it strictly being used in the lab today, or are there instances where it's being tested in production? I think what, if you refer back to Sanjay today, is it's used not only on test chips and tool and technology development, but actually on mass production parts being used. So I think that we've seen in our customers the need and the ability to ramp and maintain complex leading-edge products today with DFI, with eProbe. Now, the part that I think is an open question to PDF in the industry, as the mass production matures, as you get into that, you know, decade of mass production, to what extent and at what sample rate is that virtual contrast inspection still necessary? And as an industry, we haven't gotten there, so we can't comment on how much it can scale back or how much is needed over the long term. From a mass production need today, it's being used on real products. Great. Okay. Switching gears a bit. Of the 350 customers you mentioned, can you just clarify, that's 350 Analytics customers? That's right. Okay. And can you give us a rough breakdown of those between, you know, fabs versus systems versus product customers or whatever else it might be? I think the rough breakdown is probably the first level is equipment companies versus- Yeah, yeah. Equipment companies, when we did the Cimetrix acquisition, we mentioned it. Just go over it. Equipment companies- Stand up. Thank you, Sonya. Equipment companies, when we did the acquisition for Cimetrix, at that time, we had mentioned that there were around 220 customers. So, you know, the balance is kind of the Analytics X Cimetrix customers, if you will, and then within that, I don't know if you want to comment on that. I don't even have the breakdown off the top of my head, but even, there is even some overlap with the Cimetrix customers. Look, I mean, the other way to think about it is one of the slides that we presented, which had the system and the other players in the market and kind of arrow that goes up. You, you'll see back in the slides when you go back to it. But, when we think about our presence, it's pretty strong in the equipment space. Then, you know, it's in the product guy space, it's also pretty strong. But as we start to get into the system, that's where we have some early wins, but there's opportunity there as well that we're pretty excited about. It's probably the best way I can characterize it. Okay, great. Thanks. And then finally, the recent round of trade restrictions, that is, any impact to your machinery or software that you're aware of at this point? Look, we will always be compliant with all the laws and regulations. You know, our General Counsel is standing in the room. We always work together to make sure that that is the case. Even China in and of itself has tended to be a smaller portion of our overall revenues. It's right around the mid-teens. It's been that way, and it's stayed consistent. Sometimes when it will go higher, it'll be because we had the higher volume come in. From where we sit today, you know, we're not seeing, you know, restrictions that would guide us to think about any different metrics than what we had shared with you today in terms of revenue growth looking forward. So feeling pretty good about where we are and how we're handling compliance and ensuring it with all those shareholders. You never know what tomorrow holds. These rules are, you know, widely changing. But our exposure to China, as you know, Adnan said, is mid-teens, compared to, for example, Lam, in their last call, was somewhere north of 40%. I think most of our software products are not being restricted based on their country of origin. We are fortunate that we started our Shanghai office in order to have a consistent back end and analysis, and build people up in the capabilities of using the tool, that we can serve the China market out of our China office. So the restriction on U.S. citizens being called holders, etc., we're probably less sensitive to than some of the equipment manufacturers, was publicized when a lot of restrictions went in. So yes, there's always a risk because you don't know what the rules will change to tomorrow. But where we stand today, I would say, we're probably more stable than many of the market segments in our industry. Okay, great. Sorry, and if I can squeeze in one more. On your SAM of $2 billion plus or so, you guys have, you know, this year's consensus is only $160 million-$170 million, something like that, right? Who, what company or companies or technologies has the rest of the market? Like, there's a huge chunk there between where you are and the $2-plus billion, and I'm just curious, what's the gap there, and... Thanks. Without breaking it down too specifically, what we did is we looked across market segments that PDF, recently or today or adjacent to PDF, we may compete. We looked at either the published or estimated or told to us component of that software revenue for these companies. For example, many of the CapEx companies have software, but they don't break it out on their call. We took an estimate of what we believe the internal market is on companies. Sanjay made the comment this morning that Intel uses those a lot of their tools. In the manufacturing Analytics in the ML space, that's still true today, but the trend we see, much like happened in EDA, is moving to the external. Then for the eProbe DFI DirectScan, we took a fairly conservative number as to what subset of the e-beam inspection market today, or of the e-beam market today, is inspection that we directly compete with. I have a follow-on question to that. The only public company I know of is Optimal+, that got bought by Synopsys, and they do test that competes with Exensio. Is there anybody that, you know, has a database that covers more than just test and assembly, or test, fab, and assembly, or are you guys the only game in town? Well, you don't know what you don't know. But I think what we've found, as soon as you want to go across any given silo, working with product engineers on process control and FDC, test Engineers, et c. As soon as you start trying to link, what we find is our competition is almost always the internal groups. The IT group that wants to stand up a data lake, that wants to buy point tools, et c. So we don't know of anyone that has the breadth of reach from corporate AM, design information, fab information, test information, assembly information, even some system information today. But I think that's one of the things that's reflected in the growth in revenue per customer as people use more modules. Clearly, of course, as people have more modules, we have fewer, very small customers. There's two guys and a dog buying one copy of Exensio, obviously, because they don't need multiple modules. What we found is when people look at more enterprise-level needs, not only is our competitive position better, but you get closer to being able to articulate an ROI that's more relevant to the piece. And so we see that across our product line. And we also saw this when we're selling with our partners. For example, we announced that the deal with SAP a couple of quarters ago, same thing occurred. We were able to differentiate, and we felt got a good return on that project, simply because there's no one else out there today that can offer what we can do. Then just, again, following on, you know, thinking about moving to a platform, moving to working with SAP, enterprise sale, how has that changed your sales cycle, you know, and your ability to close deals as the size of the deals have gone up? Yeah, certainly having shorthand, that's for sure. I think when you get into an enterprise deal, the cadence of the solution, that part of it hasn't changed.... I think what we find in, call it partner-related deals, when you're looking at multiple enterprise systems, what you do run into is, more timing issues that can potentially increase the sales cycle. We are trying to add manufacturing Analytics as an enterprise platform that hooks with ERP, but are you now in the process of installing your next generation ERP? So you have to wait until that's established before you lay in manufacturing Analytics. Or is it the opposite way around? They're first moving with us, and then the opportunity, in this example with SAP, has to wait until they've ingested one of those systems. I think the biggest difference on timing for a large deal isn't so much the fact that it includes partners, because enterprise-level deals take a decent amount of time anyhow. But I think the timing of the deal and the timing of engagement, in many cases, we see start to stretch out because you have to wait for certain milestones, which is a function of their ingestion of their digital transformation roadmaps. Some of the early signs have been positive. You heard us talk in, I think, what was it, two quarters ago call, about a SAP partner deal that we had done that was a seven-figure deal on an annual and multi-year basis. So, we hope it's one of others to come. Thank you. So this is a broad-based question, but I just want to hear your thoughts on it. Within the framework of comparing present day PDFS to 2019, the last conference PDFS, the other big difference is that interest rates now are a lot higher than they were back then, and therefore, the cost of capital as well. And in response, I can't help but wonder if the need to make semiconductor assets, huge capital outlays, more productive to monetize those assets as much as possible is higher than ever. And the ability to access that with a subscription to Exensio or a Cimetrix license, seems attractive in response. And I'm just wondering if either internally you've had conversations related to this kind of framework or if you've heard customers talk about it this way. Look, I mean, the one data point we can talk about is the DFI machine. Back to one of the questions that was talked about earlier. It's a higher level comment, but really, we will stay open to the customer's desired model. So far, we have stayed and stuck true to the subscription-based model when it comes to even our DFI machines. But to the extent the model needs some changing, we've had many a conversations with customers around this, we would be open to changing it. Our end goal remains larger stickiness and larger adoption of our tool, data platform, and the whole ecosystem around it. So that's kind of one comment. To your point, certainly the cost of capital and the return on that investment, the point you raised should only help. I think if you look at the trend in our industry, as the bets have gotten bigger, putting in a fab is tens of billions of dollars, that, you know, the value we believe that PDF can bring, even as an insurance policy and making sure you're a world-class ramp process control variability, is a very minor price compared to the potential value you will receive. And frankly, a bit of a point of frustration on our return on that impact we have. But to your point, yeah, I think it only helps. I think as people recognize in this expansion, we're going to go through the limitations in workforce and the need to have a return on this new capital being put to work. Yeah, I'd like to think it plays to our benefit. It makes our offerings more compelling. Thank you. Okay, if there's no more... Okay, one more question. Got to stand up straight. Unfortunately, I have two questions. The first question is on the $2.6 billion addressable market, is DFI included in that? Yes. Okay. And then, just looking at the slide where you have outlined, I guess, the revenue per customer having increased 1.85, and then kind of- Yeah. Yeah, is that, is that pretty much evenly spread across the customer base, or do you have some pretty significant outliers that are sort of driving that growth? Yeah, I mean, look, from time to time, depending on when we sign the large deals, the large customers will sway that. So when we look at it, we try to take the time variations out of this analysis by saying, "Okay, you know what? On a longer time period basis," which is why we look at this metric, not on a quarterly basis, but we cited to you on a trailing-twelve-month basis. So it tries to take away some of those effects, but overall, will some large customers tend to sway that? Yeah, they will. But then we try to take out some of the things that don't make sense to us. For example, that's why we quote that number on the average Analytics revenue per customer, as we discussed, without CCG. To us, an Analytics customer may be somebody who's just using a software piece today, maybe somebody who's using a DFI piece today, maybe somebody who's using the whole platform. You know, the timing of that could change, but measuring it over a longer time, and yes, as a customer adopts, a larger enterprise deal could best with it. Yes, it could. But to us, the goal remains increasing the total dollar, increasing the number of customers, increasing the adoption of modules, increasing the stickiness and relevant, all of it resulting in the higher recovery revenue. I think the growth, there's really three factors. One is, yes, large customers, a very large enterprise deal skew to the high side, but very small deals, new customers, startups, etc., which are not purchasing many licenses, skew to the other direction. And so that'll bounce around based on what you do. I think the second is the growth as people try to integrate more pieces of their solution than they, much like Mike O'Sullivan talked about earlier this afternoon. And then the third is in our customer base, even on a given solution, there's been a natural growth as their volumes and their need for data volumes increase, plus some price increases as well. So there's a natural growth as the industry grows, and these companies are growing. I think scaling the solution is, of course, the enterprise-level deals being offset by new entrants in the market who may start small. Have you seen anything that's like a stumbling block to getting a new module adopted at a company that is, you know, sort of, of the size that would have multiple modules? Or said another way, is there, like, a process that you can have that's a bit more repeatable to drive that? Well, I think given our size and our experience in doing enterprise-level software, there's certainly learning that we can continue to do. We're still on that journey. You know, sometimes I think that everyone who works in some aspect of manufacturing used to be a medical, help line up for the Hippocratic Oath. First, do no harm. Mm. Right? When you're looking at transitioning from historical systems to new systems, there is a strong conservatism in making sure that even if my best present system doesn't do what I want, that there's room for improvement. I am very reluctant or... Reluctance is not the right word, very conservative in that validation of pilots of transitioning to move on to a new system. And I think if you look at the timing of deals and the timing of POCs and stretching out deals, as you're going from one segment to multiple segments, that is a challenge that adds to our sales cycle. But part of it's just learning on us as well. Okay, if there's not any further questions going on, going twice. I want to appreciate everyone for taking the time for joining us. We tend to be a little abnormal in the way we run our analyst days and users group meetings, not just because we're engineers. So any feedback you have is always welcome. We hope the day was useful for you, and we look forward to talking in the future. Great. Thanks.