Morning, and welcome to J.P. Morgan's 54th Annual Global Technology, Media and Communications Conference. My name is Harlan Sur. I'm the U.S. Semiconductor and Semiconductor Capital Equipment Analyst. Also with me on stage is our Midcap Semiconductor Analyst, Mayur Ramdhani. Very pleased to have the team from Synaptics here with us today. Rahul Patel, President and Chief Executive Officer, Munjal Shah, Vice President of Investor Relations. Rahul is gonna kick us off with a brief overview of Synaptics. It's been a busy earnings season, so I've also asked the team to just give us a quick summary of the March quarter or June quarter outlook, and then we'll go ahead and kick off the Q&A. Gentlemen, thank you for joining us today. Rahul, let me turn it over to you.
Thank you very much. Good morning, everybody. A little bit about Synaptics, and then we'll touch on the March quarter. Synaptics is a semiconductor company that's been in existence for 40 years, and it's a very strong, formidable legacy of the company. I think, when I came into the company, first thing I realized is the VLSI textbook from where I learned VLSI design was and which is a textbook that's used across the world, is authored by one of the founders of Synaptics. That's how the legacy of the company is. The company came into existence first with the ClickP ad in the PCs, and even now we do a lot in the PCs with our TouchPad products.
Came the revolution about the iPhone and the touch screen.
Synaptics was at the forefront.
Yep.
In that arena. Then came the time where Synaptics pivoted to IoT, which is where we are right now. The company is now focused in areas of Edge AI and Physical AI, along with everything that they've been doing. you know, that means the company is largely build its products on three pillars. The three pillars are processing, sensing, and connecting. That's what Synaptics is all about. We build semiconductor solutions, targeting processors, wireless connectivity, Wi-Fi, Bluetooth, Thread capabilities, processors ranging from microprocessors to microcontrollers. Our touch-sensing capabilities. Our sensing capabilities also get supplemented with fingerprint as well as video interface capabilities. These are the core IP capabilities in the company, along with many other things.
This becomes the basis for us, becoming a lot more focused in Physical AI and Edge AI on a forward-looking basis. In the March quarter, we had phenomenal results. That was, I believe, a sixth or seventh quarter, if I get this right, that demonstrated year-over-year growth. We grew substantially 30 some percent in our IoT business. Our, you know, EPS grew more than 20% year-over-year.
Mm-hmm.
While our revenue was, you know, in double digits growth as well. We also guided that this year, our fiscal year ends in June, would yield greater than 40% growth in our IoT revenues year-over-year. That was our quarter. In this quarter, we also talked about, you know, a big unveiling basically in our Physical AI play. You know, we've been talking about lot of the design wins and capabilities that we have, you know, demonstrated in the marketplace in Edge AI.
In Physical AI, we came out and said, you know, in the prior quarter, we had indicated we had a design in humanoids and, you know, 90 days later, we were at 35 various OEMs engaged on Physical AI, especially in the field of robotics that range from, you know, a small, you know, robot with no display to a full humanoid. That was, what we have reported in the March quarter.
No, that's great, and as you mentioned, it's been great to see the transformation of the business right over the many years, from sensing to connectivity to compute, and then now being able to go after the new opportunities within Edge AI and Physical AI. To your point, we can start off with the IoT business, which you just articulated the team expects to grow 40% year-over-year in fiscal 2026. You've also outlined a path to sustained above-market growth across your core IoT and Edge AI portfolio, supported by the multiple growth drivers and product ramps. Could you just break out those drivers by timeframe? What's near term versus what's intermediary term?
Like specifically, how should we think about the expected contribution from some of your newer product categories, such as your Astra processor, Wi-Fi 7 connectivity family of products, integrated MCUs, wireless connectivity, some of your semi-custom products, and so on?
Yeah. On a go-forward basis, our focus has been on Edge AI and Physical AI. The core elements are our Astra processor family of products, and the wireless connectivity and our touch sensing and video bridge capabilities.
Mm-hmm.
In this area. Astra is coming to life about now, and it's going to be the growth driver for our IoT segment in fiscal 2027 and more likely in calendar 2027. If you kind of look at what we have done over there, Astra is a category that is in the class of microprocessors and microcontrollers.
Yes.
However, with a huge difference versus what you see from the peer sets that have been in the marketplace for a very long time. Astra is AI native processors. We are not building any processor without having the ability to do inference at the very far end of the edge. It's a microprocessor or a microcontroller from Synaptics under the Astra family. It comes with, you know, inbuilt neural processing capability along with other processing engines that we have. Within the Astra processor, last year in the calendar quarter four, we started sampling our first microprocessor that we had also integrated Google's Coral NPU, the NPU that we co-developed with Google Research, along with our processing engines.
That's gone into production just about now, and it's gonna see its production ramp towards the end of this calendar year and throughout 2027. We also sampled recently a Wi-Fi 7 Bluetooth 6.0 MCU with NPU in a monolithic die. If you look at our peer set in the field of microprocessors and MCUs, the likes of NXP, STMicroelectronics, Infineon, Microchip, Renesas, they do not have the wireless capabilities that we have.
Our implementation in a monolithic die on a microcontroller that not only has a microcontroller, but also has a native NPU for inference at the far, far end of the edge, along with wireless connectivity in a single monolithic die is very exciting for our customers, 'cause now they have access to a power envelope that otherwise would not be easily attained doing multi-chip implementation, a form factor envelope that otherwise would not be attained if they have to go, again, go multi-chip implementation. The BOM envelope would have been also a lot more efficient with what we have implemented. That product is in sample stage as of now, and we anticipate that going into production in 2027, calendar 2027 as well.
The semi-custom, you know, microcontroller with Coral NPU and ISP capabilities that is targeted in the field of wearables and has been co-developed with a big, you know, customer.
Mm-hmm.
That believes in building out software stacks and scaling software stacks on top of, you know, silicon. Their business is not to sell silicon, but their business is ultimately to drive their software stack into billions of platforms, and that is the opportunity that is gonna ramp in 2027, second half of calendar 2027 with this semi-custom partner of ours. The word is semi-custom. We've developed the architecture in collaboration with this big customer so that what we compile, we, you know, implement transformers, all is done in context of what their software stack needs may be in a power envelope, in a BOM envelope that makes sense.
Again, the word is semi-custom, and what that means is, although it's co-developed in many situations, we have the ability to take it to the larger marketplace, and so that also presents us another vector of, you know, revenue realization. All these aspects of Astra come into play in 2027, calendar 2027, and that's very exciting for us. Towards the end of this year, we'll be having our first implementation of Wi-Fi 8 in silicon. You can think of likes of Broadcom and Qualcomm having Wi-Fi 8 for, you know, things like access points and phones. You know, when our peer set are barely having Wi-Fi 6, I don't know how they get to Wi-Fi 8 this year.
Right.
When I say our peer set, the likes of MCU and microprocessor players. That presents a nice, you know, tailwind to our business in calendar 2027 and 2028 as well. Very excited about these aspects. Something that I have not included in, you know, our, you know, revenue plans, but I've talked about it as well, and I've also suggested not including our revenue plans, is our, you know, activity that's going on in, you know, Physical AI with especially in robotics and humanoids.
Yeah.
That has definitely, you know, surprised us, and I think it's also validating the technologies that we have in our sensing portfolio that, you know, lending extremely well in the field of Physical AI. So all of these, again, build that confidence that Edge AI and Physical AI are gonna be our forward-looking growth vectors for the company.
Yeah. We'll talk a little bit more about some of the humanoid robot programs in your pipeline. You know, you actually gave us several good examples of how the team goes to market, especially with your IoT business and Edge AI business, right? You've got this really great portfolio of connectivity, touch, analog, mixed signal, compute, as you pointed out. Some of these products are integrated, right? So you're selling both sort of compute and connectivity at the same time. Some of them are not. The team has discussed increasing content per engagement by delivering more complete solutions, leveraging things like reference designs to broader attach.
Can you just elaborate on how this strategy is progressing and sort of where do you see the sort of biggest near to midterm opportunities?
Yeah, I think, before I came in the company, we were three groups that were largely siloed, and they're operating as a, you know, a processor team, as a wireless team, and as a sensing team.
Right.
Earlier this year, we consolidated processors and connectivity as one team, from an engineering execution point of view as well as a go-to-market point of view. That team is gonna sell processors and wireless together.
Yeah.
What that means is we're gonna sell solutions. We're gonna build solutions. We're gonna build, you know, software platforms that ultimately help reduce the cost of engineering at the customers, that help scale a lot of software capabilities that would be at a higher level of abstractions just beyond our SDKs, and also leverage our open source, open developer platform strategy.
Mm-hmm.
Across the marketplace. This is where we kind of are very differentiating versus our peer set. Again, the same big names that we talked about or I have mentioned on the process side. They have a very walled garden approach on the software side. It's their environment, it's their SDK, and if you want something different, if a developer wants to come in and play, you have to sign license agreements, you have to get through the scrutiny of are you gonna be able to consume a lot of resources or not, and what it's gonna be meaning for support, you know, dimension for the company, all of that. For us, we are open developer platform, and we let the developer community build on our platforms, and the developer community, you know, in turn supports each other, basically.
That's our strategy that's very differentiating, and it's gonna help us go to market. The traditional way to go to market for the microprocessors and microcontrollers is go through distribution.
Right.
We believe the opportunity that we have with AI native capabilities in our end products and the developer strategy gives us a jump start very inexpensively related to developing a distribution strategy. Ultimately, when we are a lot more broader in our SKU roadmap and capabilities and AI native becomes a lot more prominent in the marketplace, we will definitely go on the distribution vector. At this point, I think the strategy that we have is yielding us really good design wins and design pipeline is building up very nicely as well as a result.
Let's focus on the humanoid robot program at one of your major customers that you articulated in your prepared commentary. It's a great example of Physical AI, where you're supplying touch controller/interface solutions, with content in the range of, call it, few tens of dollars per unit. That customer is now on its 3rd-generation platform and has articulated ambitions to scale to meaningful volumes. Against that backdrop, how should we think about the opportunity to expand content in future platforms as you pursue additional sockets and additional customers, right? Such as processing, wireless connectivity, where your total content could potentially exceed, like, over $100 per unit.
Yeah, I think, it's not outside the realm of possibility.
Yeah. Uh-huh.
Right now, I'll say that. Having said that, I think, the one that we have publicly talked about and mentioned, and this big customer in North America has also publicly announced, they will be doing pilots at the end of this year. It's a humanoid.
Mm-hmm.
It's not the 1st- generation of humanoids basically that they're doing. It's very well demonstrated, talked about. Our content in that platform is largely, you know, a few tens of touch controllers.
Yes.
As well as a video bridge implementation. I'll talk a little bit about both. The touch controllers are in the palm of the humanoid.
Mmm.
It's, you know, in the order of 10- 20 touch controllers in each palm.
There's two. You can see where it goes. The video bridge is a high bandwidth bus interface from the main SoC to various subsystems, including the display. That itself also is fairly rich in silicon content. You know, you add all these things together.
Yeah.
You get to a few tens of dollars, I think, we're not naming a number, largely because I personally believe this market is in the early phase, you know, the customer, this big customer said they're piloting at the end of 2026, and they anticipate going into production end of 2027. Some reports have said the first year will be one million humanoids, and we'll see what that does. Coming back to the numbers, right? If you do a few tens of dollars into this one million units in 2028, calendar 2028.
Yeah.
Right? I think you get sense of where this is going with one design. Having 35 + designs basically now, in combination of sensing capabilities, our interface capabilities, our Astra product capabilities now, and wireless connectivity, and I'll talk about Astra and wireless connectivity. Every subsystem in a robotic platform has its own MCU and/or microprocessor, and every subsystem has the need for machine learning as well as inference locally versus having to send it to the main CPU or GPU. Its reasons for not loading the main CPU, GPU, and also latency of inference basically at the edges of the humanoid or the platform. That itself is another $10-$20 portion in the Astra processor. Every time you use an Astra processor, it's $10-$20.
Yeah.
$20 of content. Robots or cobots or industrial, you know, platforms will need to communicate as they mobilize across platforms or even in your homes if you have a cobot. They will need to remain connected. It would be peer to peer, a humanoid to humanoid, or a humanoid to the internet or to the data center, and that requires a certain level of wireless connectivity, a latency equation that, you know, does not deprive of the experience that the end humanoid application has to deliver. Long story short, there's a wireless capability over there as well. Various industry reports come out and talk about this whole market in context of trillions of dollars. You know, there's a lot of forecasts out there.
It's not easy to say this is where the plane is gonna land.
Right.
At this point, I think we are not, you know, adding a whole lot in our financial models, just keeping our heads down and remaining engaged with these customers. Our touch controllers go from the palm to other locations of the humanoid, including the feet, 'cause that's where, you know, some of the sensing capabilities need to reside. Our Astra processors can go from the hand to multiple other sub-functional sections of the platform, and so is our wireless connectivity, I think largely for data communication. So-
I just wanted to clarify one thing.
Yeah.
I mean, Rahul mentioned 35, we have 35 engagements.
Yeah.
We have, we talked about three additional designs this past quarter.
Got it.
Yeah, we started, thank you, Munjal. We started shipping silicon to three additional other than the one that I mentioned as the large customer.
I see. Let's focus on your compute family of products. This is the newer, I would say as we've followed Synaptics over the years, this is the new sort of facet of technology addition and product addition to the portfolio. You gave us an example of the potential opportunities with the Astra processor, but the Astra product line, correct me if I'm wrong, includes processors and MCUs, right? I think your SR80, SR100 are also MCU-focused SKUs that are within the Astra family of products. One of the sort of key differentiators, as you mentioned, is all of these, whether it's processor like the 2600 series or the microcontrollers like SR80, SR100, they all come with the ability to process AI and machine learning workloads, right? You've got what we call neural processing engines, NPUs, right?
For your flagship 2600, 2160 platform, your NPU is called Torq, I believe that that's correct, which leverages open source technology from Google Research and integrates a lot of hardware and software sort of innovations and accelerations. Outside of some of the humanoid opportunities, if I just think about all of the Edge AI, Physical AI opportunities in front of you, like where has the team with Astra been able to see the strongest market adoption? For which applications, which products?
Yeah.
Is it skewed more towards processors or is it skewed more towards MCUs?
It's an excellent question. Also a loaded question. You know, I'll try to kind of operate at a higher level of abstraction in my response, so I don't, you know, take up too much time on this topic. I think you should think of our processing engines as engines that are very differentiated.
Mm-hmm.
Versus what's available in the marketplace, and I'll try to use examples. SL-Series is our processor class products. SR-Series is our microcontroller class products. All are AI native. Coral NPU is Google's NPU that's open- sourced. You know, Torq is our architecture that encompasses multi-processing engines along with Coral NPU. These engines are, you know, general purpose Arm CPUs. These engines are application-specific audio processing capabilities, video processing capabilities, ISPs, all in a single fabric that we call as Torq architecture. The compilers that sit on top of these platforms are co-developed with Google Research.
It's an MLIR compiler. What that does is that it's intelligence in the compiler that says, "If this is what the workload looks like at compilation, this is how the workload needs to be distributed in terms of where it goes for processing, given the intelligence of the design and the pipeline is available in the compiler." That is how this whole architecture works. Having said that, I'll share with you a few examples, then respond to the larger question of everything, traction.
Sure.
The first, I'll share with you three examples. The first example is one of, you know, us being in our family rooms basically with our television screen. Our television screen, before we turn on, gets to know that I'm a Boston Celtics fan, right? Is aware that there's a Celtics game on a particular channel, ESPN, it may be, you know, NBC or whatnot, and you don't have to worry about what channel you need to scroll to. If it is gonna be aware, contextually aware of your presence, human presence, and effectively aware of your preferences based on your prior viewing habits, it will take you, the first set of.
Mm.
You know, eyeballs will land basically on that particular channel of choice. Now, you can obviously mobilize from that position, but that is inference and being human aware, contextually aware. If in the family room, before you turn on the television, if the television knows that here is a family with kids, it auto turns on parental control. That's being contextually aware. That's being, you know, very much of a use case that we would care for, right? I'm just giving you a couple of examples. You can imagine where this goes, and this is through the AI native microcontroller in the SR-Series series of products that you mentioned. This is how AI native, contextually aware human presence works in consumer applications.
I just used television as one place, but you can think of a lot of things, your doorbell to your thermostat to, you know, how you mobilize in the home, your security, all of that, you know, is gonna see this level of inference capability that will be supported by our, you know, Astra class of products. All of these platforms would need to be consistently communicating with various places, and that requires a certain level of wireless connectivity that's integrated in this platform. That's where we go. This is, again, a consumer class application. We recently, in the last quarterly earnings call, just to highlight, Astra is also getting into medical devices.
We highlighted a medical device that does a scan for the well-being of the mother and the child during the phase of pregnancy. This scan can be done at home versus being done in a hospital or a clinic. This ultimately has certain AI native capabilities that are leveraged for the experience that comes, the level of accuracy that like, that comes to the forefront. Again, this is in medical. The third, you know, area that I would highlight is industrial. We also touched on an application, and I publicly talked about it at the conference call, is fleet management.
Through our embedded ISP capabilities and being AI-native, the ability to manage a fleet of vehicles basically in an industrial application is also, you know, at the forefront of what we are engaged in basically in design wins and stuff like that. In all of these applications, making decisions natively after being contextually aware of what all can happen if what we are sensing is happening is what our products are capable of bringing to light. Going back to the larger question, this is obviously the first few innings of our engagement in the Astra class of products in the marketplace. Like with every other market that you see in the processor world, the first set of designs that are gonna turn into revenue are gonna be consumer- class products. Industrials are slow to ramp, but longer to hold on the revenue front.
Consumers are fast to ramp and fast to turn, basically. That's how we see our business profiling on a forward-looking basis.
No, that's perfect. You talked about in your prepared commentary, semi-custom projects. You've secured one with a fairly large customer. You've indicated that program remains on track to begin sampling in the fall with initial production ramping in first half 2027, larger volume in second half 2027. I guess first question is, how significant could this program be for the company? As you expand into potentially more semi-custom solutions, what criteria or metrics does the team use to figure out, do we pursue, do we not pursue, right? Is the team currently engaged with other customers on similar semi-custom opportunities?
It is very important for us, and given my experience, very important. When you are engaging in a new market, especially in the semiconductor marketplace.
Mm-hmm.
The investment equations are fairly substantial, right? In this age, you know, it's even more than what it was maybe 10 years back, right? Having some customer skin in the game is, upfront, is very valuable.
Right.
That is what semi-custom presents. What semi-custom also presents is a design win that you know for sure is going to ramp into production. Not only the skin in the game to support the project, but also that, quote-unquote, "a bit of a shorter bet" on going to production with that silicon a lot sooner than you would have to otherwise if you don't have a semi-custom, you know, partner to go with. The word semi in semi-custom also is that we profile what we are going to do with this partner to be able to take that design to the broader marketplace. Those are the core principles.
Yeah.
Of engaging in semi-custom. skin in the game-
Mm-hmm.
-time to market, a large market opportunity with that one customer, as well as the ability to scale the product into the broader marketplace. you know, if there is anything like you cannot go wrong in semiconductor business, then it is a semi-custom opportunity.
Right.
Right? I think that's-
Right.
-why it makes a lot of sense. Your second part to the question was, you know, others, basically. Yes, there's, you know, on the core competencies of sensing, processing, and wireless connectivity in all leading capacities, you know, we draw a tremendous amount of attention from large players wanting us to do things that are very differentiating, especially as when you are building contextually aware AI native platforms and where standard products are not available. Right. We see that in data center.
Yeah.
You know, you've seen, you know, players who are doing ASICs.
Mm-hmm.
Extremely well with semi-custom ASICs in data centers. I think the same is gonna play out at the far end of the edge. We are at the forefront of, you know, that dimension growing to being substantial. Now, in going down that path, we do have, you know, opportunities that we are engaged, quote-unquote, "evaluating," not in context as much of, you know, customers having skin in the game and the ramps and all that. Also given the finite amount of resources, can we really make this into a semi-custom versus a custom?
That's right.
Something that will ultimately also further our roadmap across broader marketplaces. You know, those are key criteria in how we kind of go about, you know, engaging in, you know, semi-custom opportunities.
Perfect.
Yeah. We have just under a minute here. If you could just briefly touch on gross margins. You have several new product ramps expected to contribute to revenue over the next 12 - 18 months. How should we think about the trajectory of gross margins as these products ramp?
Well, I think gross margin is gonna be very important, a dimension that is gonna come to play in all semiconductor business, not just Synaptics. You know, we have been maintaining 53.5% gross margin, ± 1%. That's what we guide. Despite the input costs having gone up.
Mm-hmm.
The last few quarters. Based on my anticipation, you know, I don't see relief on input costs in the coming quarters. As you know, it's widely broadcasted and talked about. So, while Astra is gonna be gross margin accretive, and it's gonna ramp in calendar 2027, you know, I remain watchful on how, you know, this will play out on a forward-looking basis given the input cost equations, and how they are trending at this point, right? So this is a story for, I think, the entire semiconductor industry.
Yeah.
Astra by design, is going to be our growth engine for the future, the near-term future of the company. It is gonna be expected to be gross margin accretive. There's, you know, gonna be headwinds with the input costs that I can't ignore.
Perfect. Rahul , Munjal appreciate your participation today. Look forward to monitoring the execution of the team as the year unfolds. Thank you very much.
Thank you very much.