Anna Wakeland (SNL): Hey, Jeff, how's it going.
Jeff Nelson: Oh, yeah.
mark: Hey!
Jeff Nelson: Hey!
mark: At least there's 2 of us.
Jeff Nelson: I think a few more on here, but 5 or 6.
mark: I see. Sorry my computer was starting up. I thought it was just 2.
Jeff Nelson: Actually a few people are coming on, I think. Probably wait for others. Valerie, wait for a couple of the other
Jeff Nelson: meerkat meerkat pis. Let's see who ends up.
Valerie Taylor: Yes. Hi, Jeff.
Jeff Nelson: Hey! How are you, Valerie.
Valerie Taylor: I'm okay.
Jeff Nelson: You're just okay.
Valerie Taylor: Yeah.
Jeff Nelson: I guess that's true.
Valerie Taylor: Thank you.
Valerie Taylor: Okay.
Valerie Taylor: And and I I know I sent it out to the to the Via group. But,
Valerie Taylor: I'm I'm not certain how many can make it today? So.
Valerie Taylor: Yeah, in this same like to this seems like the peak week for vacations.
Jeff Nelson: That might be. Huh!
Valerie Taylor: Yes, I got a number.
Grzegorz Deptuch: Not next week.
Valerie Taylor: Well, some some people take a 2 week.
Fu Hwei Hong: So.
Fu Hwei Hong: okay.
Jeff Nelson: My, I think Paul is supposed to be on, or let's see.
Jeff Nelson: I know Angela wasn't gonna make it. I think.
Fu Hwei Hong: It's.
Jeff Nelson: Somebody has a loud background that
Jeff Nelson: just give it maybe just a couple more, another minute or 2, and then we can get going.
Valerie Taylor: That'd be good.
Jeff Nelson: See who's here.
Jeff Nelson: Let me just check who's gonna make it? And then from now we can get started.
Jeff Nelson: Start at 4 0, 5.
Jeff Nelson: So, Valerie, are these mostly folks from your team that are on here? The names I don't recognize or other teams.
Valerie Taylor: I think a combination, because I know Adarsha is from Bea but there, it looks like other teams, and I'm not certain
Valerie Taylor: with mark.
Valerie Taylor: So there's and yeah, there's a few pages from Via, but it's probably a combination.
Jeff Nelson: Okay. Well, I might as well get started and see you.
Grzegorz Deptuch: There are a few folks from Alpha as well, so.
Jeff Nelson: Oh, okay, good.
Jeff Nelson: Let me share my screen here.
Jeff Nelson: Think I did that right.
Jeff Nelson: can you? Can you everybody see that.
Valerie Taylor: Yes, it. It's not in presentation mode. But we can see the screen.
Valerie Taylor: Okay, now we see your. We see the presenter mode. So you might wanna.
Jeff Nelson: Switch. Yeah.
Valerie Taylor: Of the display.
Jeff Nelson: Is that fair?
Christian Mailhiot - Sandia National Laboratories: That's okay.
Jeff Nelson: See the other one. But that's all right.
Jeff Nelson: So thanks for joining us. And hopefully, there's a few folks from
Jeff Nelson: some of the other meerkat teams. And so what I'm going to tell you a little bit about today, what we're doing in the Nsr Chip Project. This is the Nanoscale Research center for heterogeneous integration platforms, and I'll tell you a little bit more about
Jeff Nelson: who's involved in a second. And of course I'm not going to go through all 44 slides. But what I'll try to do is just kind of let you know who's involved.
Jeff Nelson: What of our you know? What of our what our challenges are what we're going after, some of the materials that we're looking into and several other aspects. And so these try to give you a feeling for what we're up to.
Jeff Nelson: So here, I want to start with this
Jeff Nelson: and it kind of gives a little background to how this project got started. This is an applied physics reviews article that has been put together
Jeff Nelson: by this team.
Jeff Nelson: and led by Zuming Liu. I think Zuming is on the line, and he's going to talk a little bit.
Jeff Nelson: Later in the presentation.
Jeff Nelson: But we had been working together for roughly
Jeff Nelson: little over 2 years before the call came out trying to understand how the 5 nanoscience centers
Jeff Nelson: could work together and identify some research priorities and capabilities that would be important for the microelectronics community. So when the call came out.
Jeff Nelson: this was really it was quite timely, because we were kind of ready to, you know, propose the research that you'll see in a little bit. But this is a review article. Maybe our 1st publication from the Nsrc. Chip team. It's been submitted. I don't have dui yet, but zooming might have it, but I don't yet.
Jeff Nelson: but it really. It's about a hundred page review article that
Jeff Nelson: tries to characterize all the capabilities that are housed at the
Jeff Nelson: Nsrcs. And look at sort of fundamentals and synthesis metrology, fabrication performance considerations for next generation microelectronics. So
Jeff Nelson: I won't show a lot of results in this presentation, since I'm just trying to give you a feeling for
Jeff Nelson: what we're up to, and maybe how you can plug in and interact with our team.
Jeff Nelson: But there are a lot of results in this article that I can send out the link eventually when when we have that
Jeff Nelson: so here's the here's the group, and we have, like, I said,
Jeff Nelson: on our team sent, which is where I'm at, and the molecular foundry
Jeff Nelson: at Lbnl center for nanoscale materials at Argonne Center for functional nanomaterials at Brookhaven, and then Oak Ridge Center for Nano phase material science. All 5 of the nsrcs and then we have contributors from.
Jim: Yeah.
Jeff Nelson: Mit, and then mit Lincoln labs, and then Fermi National Accelerator lab
Jeff Nelson: to help kind of drive towards application important for for DOE.
Jim: It's all.
Jeff Nelson: And please stop if you have any questions as you go along the way. This is kind of how we're structured. So project management structure and team members. So I'm the director.
Jeff Nelson: Christian Melo is also Melio is on the line.
Jeff Nelson: He's out at Sandia, California, and of course, has lots of experience in semiconductor physics
Jeff Nelson: over many, many years like myself and
Jeff Nelson: Anna Wakelin on the line is our project manager and really helping out keeping the team moving forward and
Jeff Nelson: us on track.
Jim: Really that.
Jeff Nelson: And then we break up into 4.
Jeff Nelson: Thrusts.
Jeff Nelson: Somebody needs a mute in the background.
Jeff Nelson: Thanks.
Jeff Nelson: into 4 project thrusts, and I'll tell you a little bit about those as we go along. And so the 1st one is focused around synthesis and processing integration that's led by Jinkyung Yu up at synth los Alamos.
Jeff Nelson: The second one is sort of now bringing the materials together into and looking at multimodal interactions. That's Ani samant at
Jeff Nelson: Argon, Cnm. And then, in terms of our use. Case and Ml. Framework. Farah Fahim at Fermilab
Jeff Nelson: leads that thrust, and then Nez Domingo at Cnms, at Oak Ridge, leads our crosscutting characterization thrust. Other contributors are listed here, and I'll give you some contact information later. Changyeong Nam at Cfn. Ricardo Ruiz, out at the
Jeff Nelson: foundry zooming lose again on the, on the phone, on the call here today, and we'll talk to you a little bit later.
Jeff Nelson: Way pan in San Diego, California.
Jeff Nelson: I ping Chen up at St. Los Alamos.
Jeff Nelson: cattle and Sparbarou at Sandia, Livermore.
Jeff Nelson: Sabika, Sodamani, here at Sint, in Albuquerque.
Jeff Nelson: and Chris Jose at Lbnl Als Light, source, and Jiwon Kim at Mit, Dan Friedman at Lincoln labs and
Jeff Nelson: stop on our wall here. It's India.
Jeff Nelson: So those are all the main team members. And of course, now they're
Jeff Nelson: assembling postdocs and other folks that are working with them at each of these sites and on the thrusts.
Jeff Nelson: Of course, as we all know, you know, we're facing unprecedented energy efficiency challenge with
Jeff Nelson: microelectronics. I think we're all familiar with that with
Jeff Nelson: you know, all of the data centers and AI and
Jeff Nelson: autonomous driving and Internet of things is all driving
Jeff Nelson: a need for more and more efficient microelectronic technology.
Jeff Nelson: And of course, AI is putting a big strain. And these are just sort of predictions that I'm sure most of you have seen in terms of the requirement for power. That's why a lot of new venture. Capital folks and others are investing in data centers and power centers to power the data centers.
Jeff Nelson: And this you know, energy for AI training. And you know, an inference. Eventual event inference is kind of reaching
Jeff Nelson: a really significant fraction of the global energy budget. And we all need to do something about that. And I think the 8 projects within meerkat are trying to tackle
Jeff Nelson: one aspect or the or the other, whether it's a materials and device, integration, challenge computing architecture and many, many different aspects. And we're all going after a different part of that.
Jeff Nelson: So of course, it's good to be on the phone with everybody, so that we can start to pull these things together and see how we can leverage and take advantage of what we're all doing.
Jeff Nelson: And so this this view graph tries to summarize what the Nsrc. Chip is doing in a big picture
Jeff Nelson: course. As you know, you know, the Cmos scaling has been going from.
Jeff Nelson: you know, 100 nanometers, the the very
Jeff Nelson: most cutting edge from Tsmc. On the order of, you know, 2 to 5
Jeff Nelson: nanometer gate lengths, and pushing to go lower and lower into sort of the atomic limit.
Jeff Nelson: And you know, that's spurring other research, because that, of course, is getting harder and harder.
Jeff Nelson: And
Jeff Nelson: there's sort of 2 pathways that this is taking sort of a heterogeneous integration pathway of advanced packaging and Chiplet architectures, and some of that's being
Jeff Nelson: worked in meerkat as well, and then a sort of continued Cmos scaling and innovation.
Jeff Nelson: And so you know, from our perspective, you know, we're not, gonna
Jeff Nelson: develop a whole new semiconductor technology. We really need to
Jeff Nelson: push to integrate with silicon Cmos and and
Jeff Nelson: and we're trying to to do 2 important things there.
Jeff Nelson: Our team. One is develop new advanced characterization capabilities. To look at these atomic scale.
Jeff Nelson: You know, features that we're all gonna need to look at as we integrate, you know, 2D materials and other things, and then do an application to show the value of what we've done.
Jeff Nelson: For you know, advanced high energy physics detectors. And that's where Fermi Lab comes in. I know several of the other projects in Meerkat have kind of a similar similar goal.
Jeff Nelson: And you know, we can really use a lot of
Jeff Nelson: interaction collaboration with the other teams to to try to get that right in terms of you know what we're doing on materials device. What we're really thinking about is developing
Jeff Nelson: integration options into this huge semiconductor
Jeff Nelson: train in terms of you know, Chiplet architectures and Hi and Cmos scaling
Jeff Nelson: where we're trying to look at.
Jeff Nelson: you know, 2D materials for electronic
Jeff Nelson: applications, wide band gap materials for sensing communication. We're looking at 3, 5 optical materials. We'll give you a tiny bit more details in a moment, and then neuromorphic materials for artificial intelligence. Of course, many of these
Jeff Nelson: there's, you know, very active areas of research. And I'm sure many of your laboratories have efforts
Jeff Nelson: along these lines.
Jeff Nelson: They're really some major
Jeff Nelson: challenges to be able to actually integrate these in to, you know, a Cmos manufacturing setting
Jeff Nelson: still sort of defects and interfaces and things like that are
Jeff Nelson: getting in the way of these things actually being adopted. And I don't know if you folks saw this. But there was just an Rfi out from the
Jeff Nelson: Natcast National, you know, semiconductor technology Center
Jeff Nelson: operator looking for information pertaining to basically this problem here, adopting these novel materials into a Cmos
Jeff Nelson: flow. And you know what the challenges there are.
Jeff Nelson: So that's really what our team is going after.
Jeff Nelson: And just to kind of summarize what those research research challenges are broken up into into 4 overall areas where you know the 1st challenge, of course, is integration of non-silicon materials with different or incompatible processing conditions and physics.
Jeff Nelson: terms of charge, spin thermal transport as well as atomic defects and interfaces. So that's 1 thing we need to.
Jeff Nelson: of course, focus on in this project. And then the second part is then, as you bring these together.
Jeff Nelson: being able to understand.
Jeff Nelson: You know, the interfacial properties of integrating the 2D material with a 3, 5, or neuromorphic material.
Jeff Nelson: 3rd part would be developing these physics informed predictive
Jeff Nelson: models, you know, based on experimental demonstrations
Jeff Nelson: and trying to get away from kind of a expensive trial and error.
Jeff Nelson: Search and parameter space. And then finally, 3rd challenge is having.
Jeff Nelson: you know, advanced characterization, operando multi physics probes to be able to look at complex and varied interfaces.
Jeff Nelson: You know, those challenges are broken up into. As I said.
Jeff Nelson: 4 different thrusts with the 1st one is synthesis and process integration. So there, we're really trying to establish, of course, the science and foundational epitaxy of
Jeff Nelson: monolithic 3D. Energy integration, and be able to control the defects and material functions at the level that
Jeff Nelson: they could be incorporated into Cmos manufacturing flow and expected outcomes. There
Jeff Nelson: are, as you see here, complex energeous materials and arrays and interfaces, then the second part and thrust 2 is about multimodal interactions and bringing those individually optimized materials together
Jeff Nelson: into stacks of 2D. And 3D. Materials, for example.
Jeff Nelson: and being able to understand. You know how that interface
Jeff Nelson: properties change and affect each other's performance and any new defects, and how that affects the device behavior.
Jeff Nelson: The 3rd thrust, then, is around. As I mentioned, use cases and machine learning framework
Jeff Nelson: to help accelerate, you know, basically take all the process data
Jeff Nelson: performance and characterization data and build a
Jeff Nelson: Ml framework that we can use and be predictive. And finally, the crosscutting characterization.
Jeff Nelson: Thrust here, I'll just kind of capture a little bit more details of each of the thrusts
Jeff Nelson: and thrust one around synthesis process integration. This kind of breaks out
Jeff Nelson: the material families that we're looking at. There's 4 different sub thrust A, BCD.
Jeff Nelson: As I mentioned before, why, bank app. 2D.
Jeff Nelson: Neuromorphic and 3 5 materials. And these are representative
Jeff Nelson: materials that the team is starting with, and of course, you know, these will evolve over time, and as the project goes forward we'll sort of narrow the scope.
Jeff Nelson: Depending how the integration and process, you know it's going.
Jeff Nelson: And so these are some examples of, you know.
Jeff Nelson: 2D materials. We'll be looking at neuromorphic materials and as well as the 3 5 integration
Jeff Nelson: course. One is for sort of high thermal connectivity and detectors.
Jeff Nelson: 2D materials more, for you know, 2D logic gates and
Jeff Nelson: neuromorphic materials for defect driven, you know, neuronal materials. And then opt electronics on the 3 5 side. So there's sort of where we're starting. But there, there are other materials operation.
Jeff Nelson: you know, options, that the team is looking into right now.
Jeff Nelson: and I'm sure many of your projects have some overlap with all this, and I think that'd be a good thing for us to identify and and kind of work together
Jeff Nelson: in. Thrust. 2 again. As I said, we take the materials that we developed, and and
Jeff Nelson: at least we think are integratable processes, and then bring those together into different stacks.
Jeff Nelson: And that's kind of shown this blown up on the side of where people think they'll be starting in terms of
Jeff Nelson: single crystal diamond, integrating with 2D. Materials, and thrust 2 A and see several options in thrust, 2 B of integrating different
Jeff Nelson: 2D materials, 2D. Materials and diamond, and then thrust. C is integrating.
Jeff Nelson: you know, anywhere from one to a couple different materials in terms of diamond oxides and 2D. Materials and same thing on the
Jeff Nelson: integration of 3, 5 materials with 2D. So this is kind of where we're starting. But, as you know, it's the beginning of the project, and these will evolve over time depending on success, and and how the team's working together
Jeff Nelson: and thrust 3, the use case and Ml framework, one broken out into in detector sensing.
Jeff Nelson: And I believe Farah is online. She'll say a little bit more about that later if she was able to make it.
Jeff Nelson: And then implementation of that use case on the other side is developing this predictive Ml framework
Jeff Nelson: and neural net and circuit simulators.
Jeff Nelson: This kind of captures what we're trying to do in the crossguiding capability characterization capabilities led by Nez Domingo at Cnms Oak Ridge.
Jeff Nelson: So sort of 3 overall areas
Jeff Nelson: one is what she's calling monitored.
Jeff Nelson: which is the synthesis of sort of ultra clean interfaces where be able to stack these things in their scanning probes?
Jeff Nelson: And do a variety of experiments, and then
Jeff Nelson: in operando in terms of probing buried interfaces, things like photoluminescence or Singletron, X-rays and Nanoarpus
Jeff Nelson: at Als and the foundry and the quantum press integration
Jeff Nelson: tool that's at Cfn Brookhaven and it's a multifast electron microscope out in Sandia, California.
Jeff Nelson: on chip testing platforms more in situ
Jeff Nelson: capacitive Afm Nano X-ray. You know all the all standard probes as well as
Jeff Nelson: what we're calling a compact platform that zooming will say a little bit about in a few minutes.
Jeff Nelson: That's kind of you know, general overview of where we're going and what we're trying to do.
Jeff Nelson: I thought it would be helpful for everybody. You know. I can send send this out.
Jeff Nelson: As many of you may know.
Jeff Nelson: You know DOE has asked us to pull together
Jeff Nelson: all 8 of our projects into a cooperative collaborative center that we've called Meerkat. As part of that
Jeff Nelson: all of the pis. We wrote up a collaboration plan and defined these 4 working groups.
Jeff Nelson: and the 4 working groups are seen there on the right materials and devices all in Hi and characterization.
Jeff Nelson: Working group 3. Methodology, methodologies, architects, intelligence, sensing, events being. That'd be something more. Valerie, who's on the line is going to be focusing on. And then.
Jeff Nelson: 4, th one is around workforce development. And so I put up all of our team members and their emails and sort of map them into
Jeff Nelson: these working groups. So, as you might imagine. Given what I've just talked about.
Jeff Nelson: we have a lot of folks that would be interested in collaborating, working.
Jeff Nelson: working within working groups, one and 2,
Jeff Nelson: and those folks are listed there and then we have a number of folks, Catalyn, Daniel Friedman and Saban.
Jeff Nelson: who are more aligned with
Jeff Nelson: working group 3. And so this just gives you some idea about how we, how we might begin to collaborate and cooperate within within these groups
Jeff Nelson: or in the project. Sorry.
Jeff Nelson: So what I wanted to kind of end with and and transition to a Zoom Meeting, and Farah said. If she's on the line.
Jeff Nelson: is I, you know, along with all of the
Jeff Nelson: potential materials and device characterization, fabrication, things like that, opportunities to collaborate.
Jeff Nelson: I thought I just mentioned. 2. 1 is this cross cutting in the crosscutting capability, characterization, capabilities, what we're calling our compact characterization platform.
Jeff Nelson: And then the second part would be having farah say a few things about in detector sensing, and thrust 3, which I know shares features with many of the other projects.
Jeff Nelson: So I think that's where I'm going to stop. And I could take some questions before we maybe transition to Zooming to say a little bit about compact
Jeff Nelson: any thoughts questions.
Jeff Nelson: Okay?
Jeff Nelson: And I'll let.
Jeff Nelson: So, Ming, do you want to take over for a couple of slides?
TzuMing Lu: Yeah, sure, I'll start sharing the slides.
TzuMing Lu: I can. You see the slides now?
Jeff Nelson: Yeah.
TzuMing Lu: Okay, yeah. Yeah. So I'll talk about see if I can go to full screen.
TzuMing Lu: The
TzuMing Lu: is this, good? Yeah. Okay? So yeah, I'll talk about the compact project that we're working on. Under this. Nsr, chip program compact is co-designed multimodal platform for accelerated characterization and testing.
TzuMing Lu: It's going to be a collaboration. And it is a collaboration between all 5 Nsrcs under DOE
TzuMing Lu: So the motivation here is actually quite simple. We have been working with a lot of magic materials that that people claim can be very useful for a lot of microelectronics applications. You can make magic widgets out of those
TzuMing Lu: And here at Nsrc we collaborate with a lot of university groups and internally as well, so that we have the opportunity to work with all these materials. So we have a lot of experience of handling with these very exotic stuff, but also that that's also one of our pain points, because for each material we have to figure out a very special way of working with them.
TzuMing Lu: And then there are so many different properties that one can probe out of each material. I can look at the electrical property.
TzuMing Lu: the magnetic property, the thermal, the structural, optical, mechanical, and chemical.
TzuMing Lu: So it's going to be a very challenging task, trying to understand a material very well. It's very time consuming thing, right? There are so many properties to interact with the world or the material to interact with the world
TzuMing Lu: as you want to characterize them.
TzuMing Lu: There's also this inverse problem that
TzuMing Lu: very often people don't think about. If we're going to use this material in microelectronics applications. Very, very often we have to consider, how is that compatible with existing silicon C. Moss, right? At least for the immediate future.
TzuMing Lu: We're not going to replace Cmos in completely. So most likely they'll work together so that we have more functionalities. And then more
TzuMing Lu: interesting is that we can do. But Cmos is still there. So adding that additional material, how is that going to change the second Cmos part? Or how is that compatible with the fabrication?
TzuMing Lu: Right? So now, going back to the materials characterization, our standard methodologies right now is that we have this material. And if I want to understand the electrical properties, then I will take that material and then do some fabrication to turn that into a electrical testing device.
TzuMing Lu: and that might be months of development, of fabrication. And then, finally, I can do that experiment same thing for thermal properties and another piece for doing optical characterization, and they may have different fabrication paths so that they're all developed separately, right? And same thing for
TzuMing Lu: the structure categorization as well. So that brings us a lot of challenges. Well, number one, do we have enough growth, repeatability, so that we have confidence saying that well, simple a for electrical is the same simple, at least nominally as simple. B for thermal, simple C for optical and simple D for Tn characterization.
TzuMing Lu: That is not an easy question to answer.
TzuMing Lu: Another challenge. Is that? Well, to do these simple prep and fabrication, we unavoidably have to do something to the material. What is the impact? And then, if we have to do very complicated, simple preparation.
TzuMing Lu: Sometimes the you lose the material properties that you want to actually measure, or you lose the intrinsic things that you want to characterize.
TzuMing Lu: And finally, another challenge is that there are so many different measurement protocols, especially across different labs, different groups, that the measured properties. Sometimes it's a little bit hard to compare, because you don't know whether people use this current to measure this property or that field to measure that property.
TzuMing Lu: So what we would like to bring to the world. Is this compact widget?
TzuMing Lu: What should this thing do if we can make a wish? Well, ideally, this can be used for synthesis of
TzuMing Lu: exotic novel material.
TzuMing Lu: and then it should require minimal processing and has minimal impact on the material after the synthesis.
TzuMing Lu: And it should be able to support as many characterizations as possible, including all the characterization that people want to do.
TzuMing Lu: There should be standardized measurement protocols, so that there's no ambiguity when we compare results. And finally, ideally, this thing has to be, or if it can be intelligent, it knows what to do. Once there's a material on there. That would be great, and then that'll improve the characterization throughput quite a lot.
TzuMing Lu: So this is what we would like to get if if we complete this project.
TzuMing Lu: So we envision 2 types of deliverables. The 1st one is a passive, compact, substrate.
TzuMing Lu: The passive substrates can be made of silicon, for example, and then there will be through silicon vias so that we can create backside contacts. And then all the passive testing structures
TzuMing Lu: will be built on the substrate. Already.
TzuMing Lu: After all that is done, we'll give that chip to material synthesis groups so they can put down their novel, thin film or or materials that they they want to characterize
TzuMing Lu: right? And then, after that, we can do additional
TzuMing Lu: back in fabrication to process the material to some degree. But we can also go directly plug that silicon chip onto a already assembled circuit board with the right contacts, and then just load that on there and mount it on there and then all the characterization protocols are handled by this already assembled electrical system on this circuit board.
TzuMing Lu: So that way there is is minimal material processing that's that's required. There's minimal fabrication that needs to be developed.
TzuMing Lu: So then that'll increase the throughput of material development and the feedback
TzuMing Lu: as well as standardizing the characterization protocol.
TzuMing Lu: So that's the 1st type of compact substrate that we will deliver.
TzuMing Lu: The second one is more ambitious, like the silicon platform itself can actually be active, because this silicon. We know how to make circuits. So if we can make active
TzuMing Lu: application specific Ics, so then we can actually do more things to it.
TzuMing Lu: Right? So those Ics can come from commercial foundries through their multi-project wafer services, or here, at least within Sandia, we know how to make silicon cmos, either in our microfab, and then silicon mesa, or within synth as well. So that gives us a lot more flexibility in what we can do with this platform.
TzuMing Lu: One thing that we can use this to. To study is then
TzuMing Lu: well, we will build active silicon cmo circuits, and it'll be intelligent, but also that gives us a way to evaluate this inverse problem. When we add this material, when you synthesize this material, what is impact on the silicon circuit, it might not be compatible. If you have to elevate the synthesis temperature to 800 degrees C, so that gives a way to a vehicle to test that impact.
TzuMing Lu: So this will be the the more ambitious goal here.
TzuMing Lu: So this is a very rough timeline of what we want to be able to deliver. So by the end of
TzuMing Lu: the project by year 4. I think
TzuMing Lu: we should be able to deliver different versions of compact, the active kind, and also the passive kind for the passive kind. We believe we can deliver on different materials, so we can use silicon, or we can use sapphire, which might be very attractive for material synthesis that will require much higher synthesis, temperatures that silicon cannot sustain, or the circuits or the materials cannot sustain.
TzuMing Lu: we will also create different subversions of those compact platforms so that we can target different material classes and different applications. For example, if there are materials that only work at low temperatures, such as superconductors, then we'll tailor make those
TzuMing Lu: compact chips for for those applications.
TzuMing Lu: So, going backwards in the in the milestones, we will want to deliver the the passive silicon and sapphire version with the characterization capability on the circuit board level by year 3. So then, by year 2, we want to be able to deliver individual modules for different characterization, modes, non-electrical.
TzuMing Lu: And then in year one, we want to demonstrate all the electrical characterization modes, including standard electrical transport, DC, transport A/C measurement, impedance measurement. So we can do capacitive measurement, inductive measurement and also microwave spectroscopy.
TzuMing Lu: Now we want to demonstrate this on various materials that we are interested in. So, as Jeff mentioned, there are so many different materials that the the project
TzuMing Lu: explore so pretty materials, metal films, semiconductor, magnetic insulators, etc. And I think this is the place that we
TzuMing Lu: really really want to collaborate with other Nsrcs, and then also other members within the Mirror cap
TzuMing Lu: center and also other Msrcs as well.
TzuMing Lu: and then going beyond year, one going backwards, we will look at and incorporate requirements from other characterization modes, for example, optical characterization, and and then X-ray characterization, structural characterization.
TzuMing Lu: We will make the structures to be compatible with those characterizations, so that this platform becomes more versatile, can have many, many different modes, so that we can get very coherent set of properties.
TzuMing Lu: I think that pretty much captures what we would like to do for for compact.
Jeff Nelson: Thanks. Zooming! That was great. Any any questions.
Jeff Nelson: When folks yeah, hang on.
Grzegorz Deptuch: Thank you very much for this presentation and all an outline of this testing characterization capabilities. My question is a bit about resources in terms of infrastructure that you intend to use within your project, and that could be potentially shared among other projects.
Grzegorz Deptuch: So could you maybe give some, and I know there can be too much to say. But
Grzegorz Deptuch: some outline of what infrastructure you're planning to use, whether it's gonna be some sort of users facility type, infrastructure. How such infrastructure could be shared among several projects to improve efficiency of, you know, for example, characterization, because I think in several projects we do have materials involved and characterization of materials based
Grzegorz Deptuch: samples, devices, and then, even later on, going into some more complex chips.
TzuMing Lu: Yeah. So I think for for the compact itself. Our idea is that we will provide
TzuMing Lu: will develop and provide this base
TzuMing Lu: PC. Board system or electrical system, so that once the the chip is fabricated, all the interfacing.
TzuMing Lu: all the interfaces will be standardized. So we you just need to put that chip onto this
TzuMing Lu: machine if you will. And then there should be standardized software that will communicate with this circuit board, and the circuit board will then control all the analog and digital circuits to do all the required measurements. And of course we can
TzuMing Lu: customize all the hardware so that the machine can do different things for different purposes. So I think this is where we are very excited to to collaborate with different projects. So yeah, if there are any
TzuMing Lu: ideas that you think which incorporates. This is a great time since, because we're designing and then trying to plan out the prototypes.
Valerie Taylor: And.
Jeff Nelson: That? Answer a question.
Jeff Nelson: Kregor.
Grzegorz Deptuch: Yeah, I think.
Jeff Nelson: So we we're going to use synth. And then the larger mesa Fab at
Jeff Nelson: at Sandia, and then outside vendors.
Grzegorz Deptuch: Thanks. Thank you.
Valerie Taylor: Yes.
Archana Raja: Consistently.
Valerie Taylor: Salary.
Archana Raja: Sorry go ahead.
Valerie Taylor: Okay, I just had a a question related to Gregor about the characterization. So this is Valerie, and and that is to
Valerie Taylor: So you mentioned the the scent. And what you're doing there. But I was wondering, too. Are you also using other facilities like, for example, the light sources. In some way. Okay, so, and.
Jeff Nelson: So yeah, zooming could say some more. But, Valerie, when the original idea came and kind of what should be on there, we have input from, of course, argon, you know, light source. Berkeley neutrons always, you know.
Jeff Nelson: Brookhaven tried to get their input on how they would integrate those capabilities into this one. Is that fair to me?
TzuMing Lu: Yeah, I think that's totally fair. So we we do want to include as many characterization needs. On this platform. So the electrical is the the one that will go after. First, st because that's what we know how to do, and that's probably the simplest as well. But we definitely will consider. And then, yeah, we have had conversations with the light source at Berkeley. And then also the microscopy.
TzuMing Lu: Okay
TzuMing Lu: at Oak Ridge. So yeah, we definitely are, including all the ideas they want to have on this platform, for example, I think Oak Ridge wants to have
TzuMing Lu: active
TzuMing Lu: platform for their transmission microscopy experiments. I think that's definitely a great direction, so that you can probe materials with those signal lights while being examined within. Ta, so yeah, those things are are considerations that we will incorporate
TzuMing Lu: in the design phase, and then we'll try to implement that in year 2.
Valerie Taylor: Okay.
Jeff Nelson: X-rays at Aps, or something right?
Valerie Taylor: Right? Okay? So cause that's what I thought about when Gregor mentioned about
Valerie Taylor: different user facilities. And then would this data, the characterization be archived and available in some database or and yes, so that question, too.
TzuMing Lu: Oh, that's a great question. Yeah, we probably need to look into that. But yeah, I haven't put much thought into that one.
Valerie Taylor: Okay? Cause. Yeah, that that would be good for sharing. If there's some way to archive, because we we're looking at it from a Co design standpoint. And so having the characterization available would be really good. Because I see you mean on here. So okay.
Jeff Nelson: So is that dollar. Is that something that that you guys could interact with us on with?
Jeff Nelson: We probably could do it, but probably do it clumsy and a lot slower than than I'm sure you guys are set up for.
Valerie Taylor: Oh, in terms of I was, gonna say, we could utilize. And we your characterization that's available.
Jeff Nelson: Oh, sure! Sure!
Valerie Taylor: Yeah, but we would, you know. So in terms of having a database, we're happy to collaborate. But it's not where we have a database, either. So I was wondering. Given that was a a major component. If you were having that that database.
Jeff Nelson: I think the best way would be to have it somehow available so that the other teams have it. If they're gonna need co-design
Jeff Nelson: data of materials and devices and things.
Valerie Taylor: Right?
Valerie Taylor: Yes.
Jeff Nelson: Gotcha.
Valerie Taylor: Goodness.
Jeff Nelson: Other questions.
Archana Raja: This is Archana Raja from.
Jeff Nelson: Yeah, yeah. I was hoping you'd ask some questions.
Archana Raja: Yeah, yeah, cause tab I was pinging tab, tab, are you there?
Archana Raja: Okay, he seems to be. But but yeah, and I are growing.
Archana Raja: no, we're working on a number of 2D materials that are a bit different from what g 1 might be making. So, Ted Tab, are you? I see you on mute yourself.
Tevye R. Kuykendall: I'm here, I'm here. Can you hear me.
Archana Raja: Sorry. Yeah, I was going to say, I don't want to speak for you. But maybe if we go back a couple of slides.
Archana Raja: yeah. Here. Yeah. No. So yeah, yeah. The passive, compact substrate.
Archana Raja: I mean, I was just curious what the
Archana Raja: you know limitations on the deposition. Or if, say, Ted were to grow something on it like
Archana Raja: what's the thermal budget of these substrates right now?
TzuMing Lu: Yeah, so that's that's 1 of the biggest concerns and then you probably know, Ji Huang. So we actually have a conversation about growing 2D. Materials. So I think, for from what he told me.
TzuMing Lu: some of the materials can be grown at temperatures below 500. So for those we can probably go with silicon substrates, which we know how to process fairly well, so we can easily do all these backside contacts and then pass away the front side with
TzuMing Lu: dielectric layers, so that it doesn't affect the synthesis
TzuMing Lu: for materials or systems that need to go to higher temperatures. One thing we are considering is to produce sapphire based platform so that it can survive at higher temperatures. That also means all the metals and dielectric will have to survive at those temperatures as well, so that constrains what materials we can use. But I think we have enough options to to do that.
Archana Raja: So what does the sapphire based substrate do? Exactly, I mean, how does that look compared to the.
TzuMing Lu: It's still just a passive substrate. It's just that. It's harder to to machine to silicon. But if it's silicon, we know how to do all kinds of processing, and then drill holes from the backside to the front side so that we can make contact very easily, so that you don't need to do anything after we can just press that sample onto a onto the socket, if you will.
TzuMing Lu: then and then we'll start doing measurements. But if it's sapphire. Then we need to figure out how we are going to create that to go through Safire via. So there's a bit more challenging in-in
TzuMing Lu: processing.
Archana Raja: Got it. Maybe Ted and I will discuss a bit more and get back to you. I mean there, there are materials that
Archana Raja: would be perhaps not super ideal at these lower temperatures. But maybe that's also important data to collect in the spirit of Valerie discussing, you know, preparing databases of.
Archana Raja: So maybe with Ricardo, we're try we're working on these very thin or very small feature sizes which could potentially fit that budget. But yeah, I don't know, Ricardo, did you have?
Jeff Nelson: I saw Ricardo was on, hey, Ricardo?
Ricardo Ruiz (LBNL): Hello, yeah. Sorry. No. Joined a bit late because I was in a in another meeting. So I think.
Ricardo Ruiz (LBNL): I I don't have any anything else to add, but I think it. It is good to see the the potential synergies here. Right? So.
Archana Raja: Yeah. So I maybe if it's like, if it's I mean.
Archana Raja: we can discuss in more detail, maybe directly with you on what's the easiest way to test this out? And what kind of materials might be interesting for this? Because doing the full device, Fab is always, you know, a big lift for us as.
Jeff Nelson: Zooming's the zoomings magic in the in the Fab. So he's the guy.
Ricardo Ruiz (LBNL): That's right. It could. It could be a a good platform, right for for various projects.
Jeff Nelson: Okay, any other questions on the characterization for now. But I think, zooming, you know, we can send this out and has contacts and mine's there, and Ricardo is there, too. He knows how to
Jeff Nelson: get a hold of us and go ahead.
Archana Raja: One last comment that I had was perhaps among the characterization things that we could do.
Archana Raja: especially that I have more experience with the 2D material side. But
Archana Raja: there are. There's some sort of information we can get from simple optical reflectance or absorption that could already tell you about disorder, and then try to correlate it with the electrical measurements, because
Archana Raja: try and see if if we do a lot of I know if g. 1 grows a lot, and we could do our more details spectroscopy on large wafers, where, for instance, if you can measure the absorption of the excited states depending on the quality of the film that actually tells you something about dielectric disorder. I have a paper in the past about this, but we've always correlated that with, for example, exciton transport, but not really
Archana Raja: electrical transport. So this might be an interesting opportunity here, if we're, you know, and and statistics always helps in this.
Jeff Nelson: Yeah, I know you had ideas about sort of. I know. We talked at one time about.
Archana Raja: Yeah, yeah.
Jeff Nelson: Low defect concentrations. That's a big. That's a big issue, too, for these 2D materials in terms of
Jeff Nelson: understanding whether you know for point defects or line defects if they're going to matter or not. Right for the.
Archana Raja: Yeah, absolutely.
Jeff Nelson: Capturing folks.
Archana Raja: And so our postdoc, Lacey Chen, setting up a new optical characterization setup. And I think it would be really cool to just, you know, chat with Zooming and and Tev and Ricardo and her to see if we can make it in a way that these substrates can actually do really well, for you know, efficient optical characterization. Just leave it overnight or something like that. Yeah.
Archana Raja: okay.
Tevye R. Kuykendall: So I had another thought. Here I also arrived a little bit late, so I think my brain was still catching up on on exactly what's being proposed here.
Tevye R. Kuykendall: I did. I do have a thought here. And so again, I'm not sure if I'm completely understanding the the graphic here. But one of the issues we're having with the project we're working on, which we're calling the filaments project is that we'd like to put a number of devices on top of our substrate and make individual contacts to those.
Tevye R. Kuykendall: And so we're kind of limited right now to the number of contacts we can wire bond directly. Trying to make like each wire bond, each device individually.
Tevye R. Kuykendall: Am I seeing something in the way here that you could maybe potentially
Tevye R. Kuykendall: do something with that like, simplify that, or make it so that we could make more contacts, or have some sort of logic that on this underside substrate, that you're spring mounting to such that you control, which
Tevye R. Kuykendall: yes, you know which contacts are being made.
TzuMing Lu: Yeah, I think the idea is that we will pre make
TzuMing Lu: all the required supporting structures like electrical wiring.
TzuMing Lu: And then for optical. We talk about gradings, and with with other groups.
Grzegorz Deptuch: Oh!
TzuMing Lu: Already on the platform before any material synthesis. So once the platform is done, it should be able to support many different kinds of measurements, including electrical. And then, if there is a need for having many, many electrical leads, I think that's something that silicon can can do very easily. So yeah, we just need to incorporate your desired layout and structures into the fabrication before the growth.
TzuMing Lu: And once that chip is there, then once they just grow the film on top
TzuMing Lu: it should already be there and then once we can route that to the signal to the circuit board, and it can do whatever we want.
Tevye R. Kuykendall: Yeah. So so the idea in our our case is, we're doing all you know, back end compatible processes. Depositing either. Probably we're starting with just tungsten metal. Doing some lithographic steps on it to make, you know. Micro slash Nano wires out of it, and then and then we wanna, you know.
Tevye R. Kuykendall: make contacts to each of these devices to supply a current through it. And we're basically what we're doing is very small micro heaters, which would then be chemically converted. Right? And so currently, what we're doing is we're making a bunch of these devices with large pads. And then, you know, maybe one common wire on one side and then, you know, individual contacts on the other. But we're having to wire bond to each of these individual devices
Tevye R. Kuykendall: right? And so if there's some way we could. I don't know if multiplexing is the right word, or like somehow do
Tevye R. Kuykendall: some sort of control on Chip or on this chip, that you're mounting it to such that we could make contact to a hundred wires as opposed to, you know, using 10 wire bonds as opposed to just being able to. Basically, you know, one to 1, 1010 wire bonds equals 1010 or 9 10 wire bonds you can get, not in our current.
Jeff Nelson: Like design. You can get 9 devices out of right.
TzuMing Lu: Yeah. So I think there are 2 2 ways of doing that. One is through the circuit board, which we can then easily acquire commercial multiplexers for routing the signals another way is to build that multiplexing capability into the silicon itself. That's a more ambitious route. So but
TzuMing Lu: the Circuit Board route it should be easily achievable.
Tevye R. Kuykendall: Yeah, yeah, I I wouldn't want to try and do that design on each of our test ships. Right? We'll call them right that we're gonna do these on. But yeah. And so I I'm liking this. What I'm seeing in terms of like a spring loaded mount where we could mount these down we could a much, you know, much simpler sort of lithographic process, and then we mount these down to something that has the some sort of signal processing in it.
Jeff Nelson: Yeah, we probably need to take for good reason. Take it offline because I think we need to get far in here for a few minutes.
Tevye R. Kuykendall: Of course. Sorry. Yeah.
Jeff Nelson: No, no, it's great, great conversation. This is what we're really wanting to do. Right?
Jeff Nelson: Okay, are you still with us?
Jeff Nelson: Yes, yes, yes, I'm gonna be very, very quick, and I try not to take.
Jeff Nelson: or I can bring it up. Or what do you.
Farah Fahim: No, it's okay. I can share it. I have it up here.
Farah Fahim: Okay, so I just have a very few slides. And actually, there are a couple of things. One is Nsr. Chip
Farah Fahim: has this thrust right where we are talking about Asics and AI and Co-design. But it's also very intimately connected with the other Msrc. The chime, which is the project that I lead. So you'll see a little bit about that project as well, and see the connection between Nsr chip and chime vias. Okay, so what we are doing in thrust 3 A is
Farah Fahim: creating chips which are Cmos compatible for growth. So this discussion just before my presentation was very, very relevant, because this might actually work out well for others as well. So what we are basically doing as a part of my Msrc. We are developing
Farah Fahim: something like 18 chiplets.
Farah Fahim: and for 3 different applications. Hep, X-ray photon science as well as for Hpcs. The high performance computing. So this is being done through 3 organizations, which is Fermilab, Oak Ridge and Berkeley, and of course there are many other universities that are involved as well. So Nsr chip actually leverages 2 of the programs
Farah Fahim: that Fermilab is involved in.
Farah Fahim: So one is the Chime Msrc center, and the other one is the natcast funded tbip, the test vehicle innovation, pipeline, which is both formula as well as slack.
Farah Fahim: So through these 2 programs, what we are planning to do for Nsr chip is create 2 sets of wafers or 2 types of wafers for further Cmos compatible growth opportunities. One is an Soi wafer, which is through the Tvip program, and the other one is the Cmos bulk.
Farah Fahim: a wafer. So with both these wafers. One of the thing that we are planning to do is back end of line integration of sensing, type of devices, and
Farah Fahim: whatever material, whichever is Cmos compatible. And what we intend to do is create process markers in both these wafers which will allow you to understand the basic health of the underlying transistors as well as passive elements based on your fabrication process. So that's 1
Farah Fahim: that's definitely something that we are aiming to do. Then the second thing we're also aiming to do is this underlying circuitry which will hopefully show us some co-design, and the idea is that these wafers will become available for growth
Farah Fahim: by year. 3 of the center, the 1st 2 years. The center is going to be focused on creating devices and everything. It's just not on Cmos wafers prefabricated Cmos wafers. Then in year 4, once all the fabrication process is completed. It's actually between both year 3 as well as year.
Farah Fahim: for the Vias project in Msrc. The chime Msrc. Is also creating open source hardware and software for chip testing. And this is called Spacegle. And the way we actually go about testing our 18 chiplets is through creating this unified interface and all plugins. And
Farah Fahim: you know, test boards which are uniform and have
Farah Fahim: very similar control and readout electronics on almost all of these chips. So, for example, the programming interface that we have decided is a Jtag interface, and all these chips will use the same Jtag type of interface. So once we develop that interface both for the design as well as for testing, we will end up reusing it for all the different
Farah Fahim: different chiplets with the Soi. There are 2 options actually sorry with the bulk Cmos process. There are 2 options. One is, we should be able to integrate the sensors as back end of line on top of the wafer as end of the topmost metal layer. But there's also the option of integrating
Farah Fahim: the sensor on the other side on the backside after through silicon via. That's because the Msrc that I'm working on is really focused on doing 3D. Integration. So once there are 2 chips that are face to face connected to each other. The only way to access them is through the backside, and that's why.
Farah Fahim: in some cases you can deposit the sensor on the top, and in most of the other cases you have to deposit the sensor at the bottom. So here is a brief overview of the Chimes Vias project via stands for vertically integrated artificial intelligence for sensing and Hpcs. And the idea is to try and build a multi-layer heterogeneous stack.
Farah Fahim: such that there is a sensor layer, a signal processing layer, a computation layer and a power delivery and signal transmission layer. So that's the goal of Vs, it's leveraging a lot of previous work that has been already done and demonstrated in several of these areas by Fermilab.
Farah Fahim: So like I mentioned, co-design is a big theme and most of the Edge AI algorithms that we are developing is actually scaling algorithms that we have already tried out. And like I mentioned, we have 3 applications in mind. The Hep, the X-ray photon science and the Hpcs. So, for example, for the Hpcs.
Farah Fahim: the AI algorithm and the chiplets that we are developing is something that has already been investigated by Berkeley, and we are, you know, basically implementing it on Chip and demonstrating beyond their 1st proof of principle. Demo, which was just the Rtl. And hadn't been implemented on Chip.
Farah Fahim: So that's like a brief outline of what we are trying to do. And like I mentioned for Viaz the deliverables include several 2 layer stack, 3 layer stack, and 4 layer stack, and you can see you can. Nsr chip will be able to demonstrate our
Farah Fahim: grow. These sensors, either on the, you know, one layer stack or on the 2 layer stack to make the 3rd layer and the 4 layer stacks where you'd have the signal processing, the data processing and the photonics
Farah Fahim: as well included. Okay, that's it.
Jeff Nelson: Thanks. Farah. Any any quick questions?
Jeff Nelson: Nothing.
Jeff Nelson: Yeah. I think probably good. Angelo wasn't able to make it, and some of the other folks. But that's probably where the connection is.
Farah Fahim: Yep.
Jeff Nelson: Well, I think we're about, and any final questions. And I can send out these slides so that everybody has some contact information. But it's also being recorded. So I think that that makes it easy.
Jeff Nelson: Alright, thanks everybody for coming. Appreciate it. See, you guys.
Farah Fahim: You, bye.