Grzegorz_Deptuch: Request. Host, okay.
Angelo Dragone: Perfect.
Valerie Taylor: Great. Thank you.
Paul McIntyre: Okay.
Paul McIntyre: all right. Well, thank you. Everybody. I think in the interest of time we should we should get going so this is the as Maurice was mentioning. This is the inaugural meeting of the entire Meerkat Center. This is one of the new microelectronics science research centers funded by the DOE office of science. This one is
Paul McIntyre: focused on the energy efficiency theme. So energy efficiency and microelectronics. And it is com composed of 8
Paul McIntyre: teams funded for a period of 4 years, each, focusing on various aspects of
Paul McIntyre: of energy, efficient microelectronics spanning from sort of materials and and device scale phenomena through circuits and art architecture to applications.
Paul McIntyre: And
Paul McIntyre: the the 8 teams all have, somewhat, you know, quite distinct, actually, mission statements as to what they're trying to do. But they're obviously areas of commonality or cross-cutting interests. And we thought collectively the the 8 pis of of these teams that it made sense to have
Paul McIntyre: a series of meetings where we basically introduce each of the teams to each of the projects to the entire meerkat center community.
Paul McIntyre: So thank you for joining this 1st meeting, and we will hear in our 1st presentation today we'll have one presentation per week for the coming, I guess. This week and the next 7 weeks we'll hear from Maurice Garcia Civeres from Lawrence Berkeley National Laboratory.
Paul McIntyre: Who's the lead pi for one of the 8 teams. This one is focused on nanohybrids. So I think I assume that Maurice will start by explaining to us what nanohybrids are, and then we'll learn about some of the exciting plans for his research project.
Maurice Garcia-Sciveres: Yeah. Thank you very much. Can you hear me? Okay.
Maurice Garcia-Sciveres: yeah. Okay, so yeah, this is the website of our project called Nano scale hybrids, a new paradigm for energy efficiency, energy, efficient optoelectronics. And so I'll try to explain what what that is and what we're doing.
Maurice Garcia-Sciveres: Actually, I'm going to give the 1st overview of the presentation. And then Jackie Yao will talk a little bit more about modeling and simulation, which I think might be of interest very broadly.
Maurice Garcia-Sciveres: So that said, Let me start.
Maurice Garcia-Sciveres: See if I can make this full screen.
Maurice Garcia-Sciveres: Ta-ta, sure. Sorry.
Maurice Garcia-Sciveres: okay, yeah.
Maurice Garcia-Sciveres: I mean.
Maurice Garcia-Sciveres: okay, so that should now be full screen.
Maurice Garcia-Sciveres: Okay? So the 1st question is, this is an energy efficiency center. So energy efficient, how? What we're doing?
Maurice Garcia-Sciveres: it's just a bit of a cartoon normally or well, more more and more these days. We use sensors that have very high data rates that. We want to collect a lot of information and then.
Maurice Garcia-Sciveres: you know, we need all the computing to process that information and a lot of power and what we would like to do is to have better sensors in a way that don't require don't produce so much data. So produce information rather than data and don't require so much computing.
Maurice Garcia-Sciveres: So we're not actually dreaming in this project of a new computer that can do the same job with less power. We actually want to reduce the job that needs to be done.
Maurice Garcia-Sciveres: But we're not inserting edge computing here to eliminate data transmission, which is, and storage, which also take up power. But still, you know, doing the same computations. So
Maurice Garcia-Sciveres: what we're talking about is a new kind of sensor where the raw output of the sensor is programmable trainable information. And if edge computing is present, it comes after that.
Maurice Garcia-Sciveres: So this is a we're talking about
Maurice Garcia-Sciveres: light sensors. So I are visible where?
Maurice Garcia-Sciveres: we can think of the sensor as a just an element where you know the photon field is is interacting with that element. And then information some pro preprocessed information comes out the end without any sort of
Maurice Garcia-Sciveres: electrical manipulation through transistors. And so
Maurice Garcia-Sciveres: now, if you consider silicon, which is an auto sensor and photosensor material, a silicon sensor outputs electric charge or current which is proportional to the illumination. This raw output has to be processed to extract information.
Maurice Garcia-Sciveres: You can't train silicon to respond differently today than it did yesterday. It does what it does once it's once it's built. And and the response is, you know, given by the material properties. Of course you can dope it and so on. But the you can't really ask Silicon, for example, to absorb Ir. It won't
Maurice Garcia-Sciveres: so Anna's oops. Sorry this is maybe the wrong presentation oops.
Maurice Garcia-Sciveres: let's see if this one's more up to date.
Maurice Garcia-Sciveres: no. Well, okay. So I don't know why this didn't change. This is supposed to be artificial.
Maurice Garcia-Sciveres: I hope I hope this is now a correct.
Maurice Garcia-Sciveres: So a nanoscale hybrid is an artificial material, so to speak, where it's actually made up of separate elements, smaller than the incoming wavelength of light. But as far as the light is concerned, it's 1 material just like silicon, because the light can't resolve these individual elements. The wavelength is too large.
Maurice Garcia-Sciveres: so
Maurice Garcia-Sciveres: Unlike a natural material. With this kind of arrangement we can independently control separate aspects of the photon matter interaction. So we can, we can manipulate the absorption separately from the transaction amplification. We can introduce nonlinearities. So in a single material like silicon. You get all these things
Maurice Garcia-Sciveres: together. You don't get to separate them out and adjust them separately.
Maurice Garcia-Sciveres: So okay, this is what I said, we can optimize sensing process, the sensing process, for example, to extract the maximum possible information from the photon field. There's a later slide on this. But in this project. What we aim to do is go a bit further than that. And to use the nanoscale hybrid also to process information ideally in a programmable way. So basically make an artificial material that can learn.
Maurice Garcia-Sciveres: Okay. So now, just to make, put that a little bit on the real axis.
Maurice Garcia-Sciveres: Here are some examples of of things. Along the way of what we're trying to make here. So
Maurice Garcia-Sciveres: so this is a pretty smart sensor, but it's you know, it doesn't actually use nanoscale hybrids. What it what it is is says the title says a tunable bipolar photodiode. So by a suitable arrangement of different materials. As shown as shown in the cartoon.
Maurice Garcia-Sciveres: you can manipulate the the band gaps, and so in such a way that photons bigger than a certain frequency will produce negative charge carriers. And and below that that frequency they'll produce positive charge carriers. So you basically get either positive or negative charges that oops that hence bipolar
Maurice Garcia-Sciveres: depending on
Maurice Garcia-Sciveres: depending on what wavelength is coming in. Of course you only get 2 choices, positive or negative, but with those 2 choices already. You can. You know, you can voltage, control the threshold at which you distinguish between positive and negative charge depending on. So the threshold wavelength, basically
Maurice Garcia-Sciveres: that is controllable by an applied voltage. And you can then program or produce some some sort of a learning device which can recognize spectral features this way, or can distinguish material. So this is.
Maurice Garcia-Sciveres: I need to add the reference for this. Sorry about that. I have some missing references. So if we upload these slides somewhere, I will.
Maurice Garcia-Sciveres: I will put the references in there.
Maurice Garcia-Sciveres: So here's another example of an in sensor learning again, without nanoscale hybrids. So in this case, what we have is an electro-optical modulator, which is in this case just, you know, a liquid crystal which can, of course, we know, rotate the polarization, and so on.
Maurice Garcia-Sciveres: But we control it with a diode, with a reading of a photodiode that's in the same pixel, let's say, and again, with some electoral feedback loop.
Maurice Garcia-Sciveres: And so so in that case, what you can do is engineer an optical nonlinearity that's low power consumption
Maurice Garcia-Sciveres: compatible with incoherent light because the photodiode doesn't care about wavelength in this case, and and it's Cmos compatible. So you can build a chip out of it. And here's a there's a chip with half a million
Maurice Garcia-Sciveres: of these pixels.
Maurice Garcia-Sciveres: and in real time you can change, for example, the do, a contrast con image contrast enhancement using this device
Maurice Garcia-Sciveres: by adjusting, you know which the pixels that receive more light reflect less, and the pixels that receive less light reflect more just automatically in real time automatically. Of course, this involves an electrical feedback loop, and what we aim to do in this project is implement such feedback and couplings between nanoscale elements. So before
Maurice Garcia-Sciveres: any transduction into electrical settings
Maurice Garcia-Sciveres: so longer term. So what I've shown so far is kind of single pixel stuff longer term we are trying to develop concepts to make networks this way. So
Maurice Garcia-Sciveres: the except that you know the inspiration for this is, of course, how vision works. So in the vision. We we don't record images, right? We we have a visual cortex actually outputs information that goes sorry the the retina actually outputs information that goes to the visual cortex, the visual cortex. That's further processing all with very low power.
Maurice Garcia-Sciveres: So for this kind of arrangement you need some sort of some sort of
Maurice Garcia-Sciveres: math or or manipulation of the of the input to to produce the information at the output. And so that's in order to do that without some sort of a circuit what we need is interactions between multiple of these nanoscale hybrids. So we just make a device. That's a lot of nanoscale hybrids, but they can actually interact with each other.
Maurice Garcia-Sciveres: so one possible oops. So so here's then an example of a network again, without nanoscale hybrids. This is again using circuits. But the point here is to have optoelectronic neurons. So I get to essentially make an artificial retina
Maurice Garcia-Sciveres: in a way that you, you know, uses sensors and then circuits to to process the information from the sensors, and then but then do that optically. So there's also lasers. So that information comes in optically and also is shared optically among the network.
Maurice Garcia-Sciveres: But again, this, this is this is, you know, not doing, not using directly interactions between nanoscale hybrids. So it doesn't actually have any scale hybrids. And so one possibility to make some sort of coupling between nanoscale hybrids is using plasmons. So this this work shows how you know
Maurice Garcia-Sciveres: prior work using, you know, using
Maurice Garcia-Sciveres: arrays of materials placed in a particular geometric arrangement to to produce different frequency absorptions and and be able to to adjust the the frequency and absorption by them by some sort of small dielectric
Maurice Garcia-Sciveres: properties between these plasmas can be used in this case to sense molecules on the surface and depending on the concentration of of the target molecule that's being sensed.
Maurice Garcia-Sciveres: You can you get more or less wavelength shift
Maurice Garcia-Sciveres: of the of the resonant frequencies in this plasmonic structure.
Maurice Garcia-Sciveres: so that could be a a way in which we can couple nanoscale hybrids together.
Maurice Garcia-Sciveres: So so now
Maurice Garcia-Sciveres: So that was kind of an introduction to, you know, prior art. And what would you know from the people who are in the proposal? You always see the pi at the top and and what we're aiming to do. And now I'm sort of switching a little bit to what we're actually doing so so in terms of the in terms of how to build, or what what nanoscale hybrid should be, or what what
Maurice Garcia-Sciveres: in in practice. There's 1st some theoretical work that
Maurice Garcia-Sciveres: aims to understand. What what kind of properties a nanoscale hybrid should have to make you know these artificial materials. So so what should be how you know in terms of quantum quantum states? You know, what are the quantum states that carry out absorption. And what what are the and how does that
Maurice Garcia-Sciveres: correspond to then transduction into electrical signals?
Maurice Garcia-Sciveres: And and you know in this work. It shows that it's it's possible, for example, to make a
Maurice Garcia-Sciveres: a detector that has a spectral, spectrally resolved response with a with a you know, essentially 100% quantum efficiency within some band
Maurice Garcia-Sciveres: when there's a frequency band.
Maurice Garcia-Sciveres: By having, you know, suitable combination of absorbers. And then and then states that carry out transaction.
Maurice Garcia-Sciveres: So this is all
Maurice Garcia-Sciveres: quantum mechanics and and calculations.
Maurice Garcia-Sciveres: there. There are experiments that then suggest that
Maurice Garcia-Sciveres: nanoscale hybrids made with one D and 2D materials can actually achieve these properties. So this is a past work, that showing that that essentially
Maurice Garcia-Sciveres: informs how we're doing things in this project. But basically all this, this work shown here uses carbon nanotubes as the
Maurice Garcia-Sciveres: device that carries out the transduction to electrical signals and then different devices to absorb light and couple to the carbon nanotus?
Maurice Garcia-Sciveres: So then, in addition to theory, then, there's modeling to actually to actually predict how devices will actually work once we make them, and how? How should we interface them to circuits? So this, this, the later talk by Jackie
Maurice Garcia-Sciveres: So the modeling tools that are being used and developed for for this project are, I think, possibly, of either, quite quite a quite broad interest in Meerkat. So so that's why we chose to have Jackie focus on this presentation later. Because, of course, we can't do. We can't dive into every aspect of the project.
Maurice Garcia-Sciveres: So then sorry I need something.
Maurice Garcia-Sciveres: So
Maurice Garcia-Sciveres: so then let me talk a little bit about the ingredients and the assembly methods that we're developing
Maurice Garcia-Sciveres: to actually make some prototype nanoscale hybrids to carry out these functions.
Maurice Garcia-Sciveres: So in terms of one d. And 2D materials.
Maurice Garcia-Sciveres: you know, we have work on lithographically defined Tmd transition metal, dical cogenite, alloids and heterostructures. This shows a picture of A of a structure that has layers of of Tmds.
Maurice Garcia-Sciveres: We're using carbon nanotubes
Maurice Garcia-Sciveres: as devices that then we have to process further, and I'll talk about later, and in this case trying to place them on Hpm. Flakes as a dielectric as a gate dielectric to have a very low noise for single photon response, and also tellurium nanowires instead of carbon nanotubes, which can be lithographically defined, and also have, you know, similar properties for transduction.
Maurice Garcia-Sciveres: So there's also for one d. And 2D. Materials. It's not only being able to produce them, but characterization is critical. And so we have an extensive extensive capabilities for an expertise, for characterization.
Maurice Garcia-Sciveres: And you know, like I said, they're extensive. You can look here foundry tools just in one area. But let me just give one example. That was done for specifically for characterizing Tmds, which is the fabrication and design of
Maurice Garcia-Sciveres: of silicon nitride membranes.
Maurice Garcia-Sciveres: You know where you can produce these Tmds. You can through this process called lateral conversion synthesis, which is what we're using. But then also analyze them with transitional transmission electron microscopy, thanks to the thin membrane. And so this a lot, this this kind of characterization, allows one to see what happens, you know, with the precursors. And then, after the after the
Maurice Garcia-Sciveres: the conversion and and understand really things like effect of temperature.
Maurice Garcia-Sciveres: Okay? So then we also need Cmos substrates, because every, all, everything, where, when we actually make these nanoscale hybrids, we have to read them out somehow, and to be able to talk to them. And this is all going to be done with a Cmos back end. So this is a Cmos plus x development. Let's say so we need.
Maurice Garcia-Sciveres: You know, our own Cmo substrates. So we have so far produced
Maurice Garcia-Sciveres: in the in the, in the previous project which was called nanoscale hybrids on on nanoscale sensors, on cmos we produced wafers on Tsmc. 130 nanometers, and on skywater 130 nanometers through different paths, and managed to get in both cases the required planarity of the surface for integration of nanomaterials, which is over one nanometer.
Maurice Garcia-Sciveres: and and we can now use these devices in this project, these these back end Cmos chips. And we're additionally starting to design for 40 nanometer Tsmc through the Dod microelectronics Commons project that will also help us produce.
Maurice Garcia-Sciveres: yeah. Yeah. Another wafer with more features that we can use for these for these for this new project, where we want to try processing as well as develop processing or in in hybrid processing as well as sensing.
Maurice Garcia-Sciveres: So then, of course, we need patterning and imaging. Again, we have lots of capabilities there. Here are some examples of
Maurice Garcia-Sciveres: patterning trenches. These horizontal lines are trenches. There's a
Maurice Garcia-Sciveres: a close-up view, and I'll again, and and then on the on crossing the trenches at a right angle are carbon nanotubes, and here you can see the individual tubes. They're very straight in parallel in this case. And
Maurice Garcia-Sciveres: and there and then this. We're, you know, this is a prototype using just a blank substrate.
Maurice Garcia-Sciveres: But then we want to do this on a Cmos chip, and there's a picture or afm of a of a trench on a surface of a Cmos chip. It doesn't look as good yet, but we hope it will look better in this 40 nanometers process
Maurice Garcia-Sciveres: and and and then, once we have carbon nanotubes on here, we can connect them to the chip below, and we can also make here, or we can add lithographically, tellurium nanowires. This is a picture that shows this, this skinny line, here is a Nanowire, and then there's an electrode and a readout, or, let's say, interconnect pad to connect it to the chip
Maurice Garcia-Sciveres: so so this is all work towards making some demonstrator that is effectively will behave, will behave like this photo diode that distinguishes frequencies that I mentioned before. So in this case we can have some carbon nanotube sensitive to one
Maurice Garcia-Sciveres: set of frequencies, and another set to another frequency. So we could. We could be you know, more we could have more bins than just plus or minus, as in the case of this of this photo diode, and of course, in a much at a much smaller scale.
Maurice Garcia-Sciveres: okay, so then. So then we have to do something more. So in this case, if we just put in carbon nanotubes is not enough. The carbon nanotube is a device that does the transduction. It's not in itself a nanoscale hybrid. We have to add stuff to it. So we need to add in particular things that absorb light of the wavelength, and then for that we can use quantum dots. And so the question is, how do you put quantum dots on a carbon nanotube and
Maurice Garcia-Sciveres: and that's where the trench comes in. So this the fact that we have the carbon nanotube is a bridge crossing a trench allows access from
Maurice Garcia-Sciveres: all around the carbon nanotube, and that allows to wrap DNA around it. And then, in this process of
Maurice Garcia-Sciveres: decoration with DNA self-assembly, we we have DNA wrapped around the carbon nanotube, and then quantum dots that have been functionalized with DNA tails, and then they can. Those quantum dots will then attach selectively, because we use the right DNA sequences to the to the carbon nanotube where the trenches. So we go from this
Maurice Garcia-Sciveres: a bunch of quantum dots and a carbon nanotube to a carbon nanotube with the quantum dots on it. There's a picture of what that would look like
Maurice Garcia-Sciveres: or what it actually looks like, and this is an actual carbon nanote with the DNA on it.
Maurice Garcia-Sciveres: So so then, you know, that's another thing we can do with self. Assembly is then place things on a Cmos chip that are, you know, very small. So instead of building
Maurice Garcia-Sciveres: nanoscale hybrids directly on a chip, we can build them in solution, let's say, and then place them on the chip. With this directed self assembly which uses this technique of peptide brushes which can be patterned on the surface lithographically, but then, wherever you pattern them, you can, then you can select, you know you can again use the right sequences, so that a particular DNA
Maurice Garcia-Sciveres: sequence will attach to a particular peptoid, and then you can. We can attach this DNA origami, so-called DNA origami. These are bread boards made of DNA origami. So so there's a lot of DNA strands in here. But it's basically a flat little.
Maurice Garcia-Sciveres: a patch of
Maurice Garcia-Sciveres: of DNA which has which has select selective attachment to some of these peptoids, and on top of it it can carry a circuit, carry a nanoscale hybrid with carbon, nanotubes and quantum dots, etc, and then can attach, you know, like the green ones could attach here, and the blue ones could attach somewhere else, for example. So so that's this is a demonstration of a pattern of these patches attached in specific locations of a substrate.
Maurice Garcia-Sciveres: So so that that's it for the very quick introduction of what is it we're working on, and what we would like to do. And so now I'd like to
Maurice Garcia-Sciveres: send it over to Jackie to explain more about the modeling and simulation tools.
Zhi (Jackie) Yao: Sure. Thanks. Maurice.
Maurice Garcia-Sciveres: I'll stop sharing. Yeah.
Zhi (Jackie) Yao: Okay.
Maurice Garcia-Sciveres: If I can figure out how. Okay, stop.
Zhi (Jackie) Yao: Okay, great
Zhi (Jackie) Yao: desktop. I'm sharing my entire desktop. But I want to show my slides on presenting mode. Is that visible
Zhi (Jackie) Yao: great.
Maurice Garcia-Sciveres: Yeah. Yes.
Zhi (Jackie) Yao: All right.
Zhi (Jackie) Yao: I'm hoping to finish within 10 min, so that we can have some time for questions and discussions. But so yeah, thanks, Maurice, for highlighting the modeling effort. So basically, I would like to give an overview of the microelectronics simulation packages that we have been built over the past years, and we're hoping that those packages can serve as a tool that is available to all of the projects within Msrc.
Zhi (Jackie) Yao: And a lot of work are in collaboration with the nanohybris team and other microelectronics team, and you can get access to the open source packages. Through this Github Link. I will copy and paste this link at the end of my presentation in the chat.
Zhi (Jackie) Yao: Okay, so here is an overview of what physical mechanism we're including, as of now, we have several packages named Artemis, electronics, Ferrox and Magnax. I'll introduce them one by one in the following slides.
Zhi (Jackie) Yao: basically, we're taking care of electromagnetics, whether in the static or dynamic fashion, the ferromagnetic materials. So that give us the switch of the magnetization, the spin wave propagation and the quantized magnet interactions ferroelectric material that enables non-volatile memory functionality, quantum transport that include electron transport in the carbon
Zhi (Jackie) Yao: tube. And we're working toward enabling it for other materials and also phonon transport.
Zhi (Jackie) Yao: And finally, the superconducting, the classical, superconducting theory represented by London equation, that allows us to simulate the electric signal flowing in the superconducting material and circuits. The key features of our modeling tool is that they're flexible.
Zhi (Jackie) Yao: portable, open source, and massively parallel. So I will introduce the details one by 1 1 key point. I would like to emphasize is that our device package, modeling tools sit in between material physics and circuits and architecture applications. In another word, we
Zhi (Jackie) Yao: look into the junction, physics and device physics, we solve continuum form of the Pdes to predict the physical interaction in the system. So we do not work on 1st principle material. We get the output of the dft simulations, which is that they predict the material properties and hand it over to us. We use the material properties at the input in
Zhi (Jackie) Yao: to our model. And the output of our model is the circuit response. For example, the device's Iv curve and the transistor's transconductance. So this is where we sit.
Zhi (Jackie) Yao: I just want to make it clear in case you get interested in using some of our models.
Zhi (Jackie) Yao: All right. So I mentioned that our code packages are portable, meaning that because we're fortunate to leverage the access scale computing product, Mrx, our code packages are functional on different Gpu and CPU platforms. If you switch from perimeter to frontier
Zhi (Jackie) Yao: to Aurora, you don't have to rewrite the code like some of other packages, do. You can just use the model and run the simulations.
Zhi (Jackie) Yao: It's accessible, meaning that it's open source. Everyone can download and use the package, and if you have questions feel free to reach out to us. It's also got flexibility in the algorithm, meaning that even though we only include this packages and the associate physical mechanisms, now, if
Zhi (Jackie) Yao: there are interest to implement new physical mechanism and the additional coupling, then we're happy to do that.
Zhi (Jackie) Yao: And we use time domain algorithms that are suitable for nonlinear problems. Because nowadays a lot of these problems are nonlinear and highly coupled.
Zhi (Jackie) Yao: We've got almost ideal Gpu and CPU scalability because we're fortunate to leverage the Mrx Ecp product. So this is an example of the weak scaling efficiency of one of our package.
Zhi (Jackie) Yao: as you can see that up to a thousand of Gpus. We're seeing a flat efficiency, meaning that there's no overhead runtime from the Gpu or CPU communication. So this package is very suitable for especially for larger problem simulations
Zhi (Jackie) Yao: alright. So at this point I've overviewed the features and the basics of the package. Now let me go through them one by one, so that you know, beyond just this fancy words. And
Zhi (Jackie) Yao: you can understand what's going on behind the scene and what equations we're solving. The 1st package I'd like to introduce is called artemis, adaptive mesh, refinement, electromagnetic solver. We solve Maxwell's equations in the fully dynamic form, and or with the coupling to the micromagnetic Llg equation listed here.
Zhi (Jackie) Yao: the superconducting classical London equation with the extra superconducting flow included, or the ferroelectric Ginsburg landau equation. We have implemented the Ginsburg Landau in electrodynamics. We have to validate it before we use it for more applications. But this is the backbone of Artemis. And here I'd like to show 2 application examples.
Zhi (Jackie) Yao: 1st on the left is a hybrid quantum circuits. We basically have a coplanar waveguide resonator, and on top of that we have a micro ferromagnetic materials.
Zhi (Jackie) Yao: which is the cobalt iron material. So if we excite excitation of both the Cpw. Resonator, which is photon and the ferromagnetic resonance which is the Magna, and we tune them so that they're in the strong coupling regime, we're able to get this anti-crossing spectrum feature, and this is from Artemis Maxwell plus llg simulation. Then we run the Maxwell plus London simulation
Zhi (Jackie) Yao: and simulate this quantum chip. This is a simplified, simplified view, because the complete view is is still proprietary, but this is a nice showcase of Artemis's capability of simulating a a larger scale quantum circuit.
Zhi (Jackie) Yao: Next, electro electronics.
Zhi (Jackie) Yao: This is for non-equilibrium quantum transport. And this is achieved with collaboration with Francois mostly. So what this model does is it solves the electrostatic poisson equation which is simplified from the full dynamical Maxwell's equation and we solve the quantum transport represented by the non-equilibrium Green's function method, the Nejf method.
Zhi (Jackie) Yao: And of course, we have to solve those 2 equation systems in a self-consistent fashion.
Zhi (Jackie) Yao: Here is one example of the model application. We have carbon nanotubes, either in the fully aligned structure or misaligned structure, and then we run the negf simulation and and post-process computed the transconductance here to our best knowledge. This is the one of the kind
Zhi (Jackie) Yao: non-equilibrium transport model that can accurately simulate the misaligned carbon nanotubes. So, as you can see, the misalignment has a significant impact in the transconductance.
Zhi (Jackie) Yao: it causes a 12% difference. If we have different kind of alignments.
Zhi (Jackie) Yao: different kinds of different level of misalignments. Yeah. So this is the the electronics code package for non-equilibrium quantum transport. Currently, we have carbon nanotube. But we're actively working toward generalizing it to more materials and more kind of particles like phonons.
Zhi (Jackie) Yao: Next, I would like to introduce Ferrox. That's a phase field module for ferroelectric devices here shows the mathematical model of Ferrox.
Zhi (Jackie) Yao: We solve this 3 equation systems in a self-consistent way. 1st is on the left. We have the Ginsburg Landau equation in the time domain that is describing the evolving of polarization in the faroelectric layer. Then we have the electrostatic poisson equation, which is pretty similar to electronics.
Zhi (Jackie) Yao: And then we have the carrier calculate the the carrier density calculation. In the semiconductors we have both the Fermi direct distribution and the Maxwell Bosman distribution. We also have the drift diffusion carrier transport implemented in this in farahx package.
Zhi (Jackie) Yao: I would like to show the negative capacitance field effect transistor applications to showcase the capability of Farrox. So the 1st figure here shows the polarization evolving as a function of time, then the lower panel in the middle here shows the farox simulated capacitance enhancement effect, which is one of the signature of negative capacitance.
Zhi (Jackie) Yao: So as you can see that with different portion of different different ratio of the mixed face.
Zhi (Jackie) Yao: Here we can have different level of capacitance enhancement.
Zhi (Jackie) Yao: If I reiterate here in the middle we have the polarization color map, the domain pattern of the polarization only within the orthorhombic phase and surrounding the orthorhombic phase, we have tetragnal phase which is nonpolar so with the depolarization effect coming from the O phase and T phase mixed up, we can get different levels of capacitance enhancement depending on the grain size.
Zhi (Jackie) Yao: And here shows the Gpu. Scaling up to this is perimeter node numbers. If you multiply by 4, we get the actual Gpu numbers. So up to about 500 gpus, we get pretty perfect weak scaling performance, meaning that we get no overhead between, no overhead coming from the Gpu communication.
Zhi (Jackie) Yao: Last, but not least, the magnex micromagnetic module for magnetic devices. We solved the Lrg equation, the Ginsburg landau. Sorry the lift, the the landau lift shit equation.
Zhi (Jackie) Yao: and we have all of the the effective magnetic field included, and those could either be fully dynamical field or quasi-static field.
Zhi (Jackie) Yao: I'm going to just very briefly introduce the scaling studies, the cutting edge, spatial, temporal, numerical discretization. We also validated against the very standard Mumak problems.
Zhi (Jackie) Yao: Then this is one demonstration how we use magnex to validate experimental data. So here we have a spin cycloid texture in one kind of material, which is the Bismus Ferrite Bfo material.
Zhi (Jackie) Yao: We have the texture set up, and then we deposit spin wave from one end of the material, and that let the spin wave propagate through, either along the cycloid direction or parallel to the uniform direction of the cycloid.
Zhi (Jackie) Yao: Then we post process and get the spectrum performance of the magnum. So here shows the spin moment cone angle as a function of the frequency, and we get the resonance at different frequencies.
Zhi (Jackie) Yao: Then we calculate the transport efficiency of the magno along those 2 different directions, and you can see that our experimental result falls in the range of the of the numerical simulation. And this is agreeing with the Bfo, the Bismus varite material properties.
Zhi (Jackie) Yao: So we're we're upgrading the magnex
Zhi (Jackie) Yao: functionality into more advanced materials, like antiferromagnetic material with more mechanical coupling. So that we can model magnetoelastic material.
Zhi (Jackie) Yao: Okay, with that, I would like to thank you for your attention. And again, if you're interested, you're welcome to check the package, and if you have questions or comments, feel free to reach out to Andy or myself. Thanks.
Maurice Garcia-Sciveres: Okay. So that concludes our presentation. And hopefully, we have some time for discussion or questions.
Maurice Garcia-Sciveres: I don't know if Paul, if you wanna share the.
Paul McIntyre: Yeah, I'm happy to chair. Are there questions for Maurice and Jackie?
Maurice Garcia-Sciveres: Or for other members of our team.
Paul McIntyre: Or other members of the team. Yeah.
Paul McIntyre: 1st of all, thanks for for the presentation. I think it was very thought provoking. I I have a I have a question. related to the you know, the kind of design principles for a nano hybrid, a nanoscale hybrid
Paul McIntyre: structure. You've shown, Maurice, that you're through the team of pis you have. You've got access to a variety of different nanoscale, one d. And 2D semiconductors. There are, you know, various kinds of transduction and memory possibilities.
Paul McIntyre: Is there a particular is there a particular kind of nanoscale hybrid that you that the team feels most interested in starting with. I mean, you've got all these interesting building blocks.
Paul McIntyre: Is there a way that that you're thinking about prioritizing which ones you put together and and for what? For what purpose?
Maurice Garcia-Sciveres: Yeah, well, the the more advanced work is in integrating carbon nanotubes with quantum dots. And the idea there is basically to have different. So these are single carbon. Nanotube is the goal. So I have a single carbon nanotube device that has quantum dots on it.
Maurice Garcia-Sciveres: and then the quant the that would be read out like a transistor. So with a gate drain source on the carbon nanotube so that so that we're we're, you know, we're moving along trying to get that
Maurice Garcia-Sciveres: or or making pro making prototypes of that that, you know, we're pretty close to having, you know, a single a prototype just for a single color of quantum dots, but the goal is to have, like 2 or 3 colors on the same pixel, so different same carbon nanotube, different quantum dots on each one. Right? So 3, let's say, 3 tubes with one with green dots, one with red dots, one. So so that's that's kind of our
Maurice Garcia-Sciveres: currently most
Maurice Garcia-Sciveres: the the thing we've done the most work on. But you know, the the issue with carbon nanotus is they're hard to deal with. So they're they're basically especially individual tubes. So they're so we need lots of gymnastics to place them, connect them, decorate them right?
Maurice Garcia-Sciveres: Sorry someone.
Paul McIntyre: Gregores has a has a question, I think, or a comment.
Maurice Garcia-Sciveres: No, I think he'll comment.
Grzegorz_Deptuch: No, I why should I be commenting, Jackie? Thank you very much for presentation.
Grzegorz_Deptuch: I may. I have many questions regarding the package simulation package, and maybe proportion between your goals. For to fabricate and to simulate so is
Grzegorz_Deptuch: is your goal to still develop the simulation package, to to treat this as deliverable in your in your project, or as a tool to simulate and fabricate. So maybe this is a kind of follow up question on what Paul requested. And because it's gonna be heading towards, you know, the prefer preferred structures, preferred methodologies, or maybe preferred even technologies. Yeah.
Maurice Garcia-Sciveres: Okay. So I I guess Jackie should take that.
Zhi (Jackie) Yao: I let me let me clarify if I understand the question. So the question is, do we plan to continue developing this code packages. Right? We definitely plan to continue developing this code package right it within nano hybrid.
Zhi (Jackie) Yao: 2 of the packages are of special interest. One is Artemis, which is the electromagnetic solver, because within nano hybrids we're interested in looking into this optical computing and photonic effects. The second package is the electronics package that is the non-equilibrium quantum transport. Because we have continued interest in carbon nanotube
Zhi (Jackie) Yao: and interactions between electrons and photons and possibly phonons. So yes, we're definitely interested in keep developing those
Zhi (Jackie) Yao: packages, but I think it does not conflict with the interest of fabrication. Right? So our goal is at the end of the project. We can deliver a self-consistent modeling plus fabrication tool packages. And this is actually continuing the initial interest of co-design. So I think
Zhi (Jackie) Yao: yes, there's continued interest. But that does not mean that we will not. We're not interested in fabricating the device. Is that what you're asking.
Grzegorz_Deptuch: Effectively. Yes, you answered my question.
Maurice Garcia-Sciveres: So, since I know Gregory, maybe I can add something is that one of the aims eventual aims is to actually is to use the one of these pipes, not not directly integrate, but use one of these packages, together with spice, simulation, or.
Grzegorz_Deptuch: So basically.
Maurice Garcia-Sciveres: To model the entire circuit of the nanoscale hybrid on a circuit on a Cmos chip. And you know, and, for example, to make a a
Maurice Garcia-Sciveres: amplifiers that have a carbon nanotube as the input transistor right? And so then then you would need the results of the of the of the simulation tools to tell you what the trend, what the input transistor is doing in order to build a model for the or to use in spice, basically.
Grzegorz_Deptuch: Thanks, a lot.
Paul McIntyre: Any further questions.
Paul McIntyre: I guess I have one question. That's maybe a little bit more on the mechanics of how your team works together. Do you? What? What is the sort of nature of the meetings that you have? And and you know, how are you? How are you kind of structured, I guess.
Maurice Garcia-Sciveres: Yeah, we're we're basically working as one group. We don't really have. We have a bit of a you know, a bit, a bit of a side meetings for specific
Maurice Garcia-Sciveres: specific things like technical details of, you know, placing quantum dots on something or technical details of lithography and so on. But but mostly we have a weekly general meeting that we all attend, and we kind of.
Maurice Garcia-Sciveres: you know, present to each other the progress of the different areas. Of course, sometimes a single meeting isn't enough, but we just move to the next, you know, move some material to the next week, or sometimes we don't have enough.
Maurice Garcia-Sciveres: But but basically, yeah, we just have a
Maurice Garcia-Sciveres: I want one. We work as one group with one meeting as far as sync syncing up every week. And then in between those weekly meetings there's kind of side meetings of a few people doing specific work, but but we always come together once a week to sort of sync up.
Zhi (Jackie) Yao: Yeah, we'll see.
Zhi (Jackie) Yao: Have the steering meeting. Maurice.
Maurice Garcia-Sciveres: Yeah, right? So so then we we yeah, we have a a, A steering group and and monthly steering group meeting for the kind of more, you know. Discuss sort of strategic strategy. And are we meeting our goals and what you know, and reporting and hiring? And so? But.
Paul McIntyre: Okay, very good. Pops.
'Pops' / NY CREATES: Hi, thanks. I really enjoyed that presentation. I was curious about 2 things. One, there have been a few people who've been working on aligned depositions of the carbon nanotubes. Is your team leveraging some of that expertise, or at least some of that know-how, such as it is. And the second question is.
'Pops' / NY CREATES: when you were functionalizing the DNA and getting the 3 colors that you were describing. You know the green, blue, and so on. It seemed to me that the number of dots that land might be a bit stochastic. Are you planning on flooding the carbon nanotube so that you know such variations
'Pops' / NY CREATES: turn out to be control, you know not not material.
Maurice Garcia-Sciveres: Yeah, let let me 1st ask. Answer the 1st part, and then Greg, who's connected, can talk about all the headaches he has with, but for the align for the align tubes. We actually used a vendor that. Had this developed this proprietary process to to place carbon nanotubes, and they just transfer them from from a fabric, from a substrate, where they fabricate them to to the chip of interest as a as a big patch of aligned tubes.
Maurice Garcia-Sciveres: So they did that for us. So we hope to continue, you know, making use of that kind of process outside process.
'Pops' / NY CREATES: Thank you.
Greg Tikhomirov: I can.
Greg Tikhomirov: Maybe maybe I can
Greg Tikhomirov: part of your question. The number of quantum dots we get per carbon. Nano chips is definitely stochastic, as you acutely noticed, and
Greg Tikhomirov: well, according to Francois simulation, you know.
Greg Tikhomirov: it doesn't really affect the efficiency too much as long as there are some a few quantum dots on the carbon nanotube. But yeah, we would like to have them more uniform. But you know this is the nature of attachment of DNA to carbon nanotubes. It's not
Greg Tikhomirov: covalent. It's based on a pipe I stacking with the carbon nanotube, and it's very hard to control.
Greg Tikhomirov: But there's not.
'Pops' / NY CREATES: Problems. First, st no, but thank you. Yeah, obviously good. 1st step. Thank you.
Greg Tikhomirov: Thank you.
Paul McIntyre: Great further questions or observations.
Paul McIntyre: Okay?
Paul McIntyre: Oh, I guess. Another question.
Grzegorz_Deptuch: Very quick question about manufacturing. Are you planning to use.
Paul McIntyre: More.
Grzegorz_Deptuch: In house capabilities to develop capabilities, or, you know, use some external capabilities, commercial grade foundry. Can you maybe comment on this? Because I mean. We know that manufacturing is coming at high price, generally speaking, and this is.
Maurice Garcia-Sciveres: The answer is both. So so, for example, of course, for the Cmos wafers, you know, fabrication, that's all done. Foundries as you know, but we've been lucky to take advantage of some initiatives like the the NIST nanotechnology accelerator that you that produced the wafers at Skywater. You know. That was free to us. Because and it was a pilot program. And then the
Maurice Garcia-Sciveres: and then the one that is upcoming through the micro electronics, Commons, Dod microelectronic commons. Also, we just pay for the waiver, but not for the masks. So because it's a multi project thing sponsored by that effort.
Maurice Garcia-Sciveres: So so so then for the other parts, for the post-processing, let's say, for example, if we need to planarize and do chemical mechanic polishing or wafer coring, you know we send all that stuff out.
Maurice Garcia-Sciveres: The Eb lithography will do in house so the you know, regular lithography. Also we can do in house. So depositing palladium, for example, contacts for carbon nanotubes, and then and then platinum electrodes on top of the palladium. All all that's done in house. The the tellurium deposition for making tellurium nanowires. Also in house.
Maurice Garcia-Sciveres: as as all the Tmd. The Tmd. Fabrication is all at the family.
Grzegorz_Deptuch: Nice. Thanks.
Paul McIntyre: Alright! Angela.
Angelo Dragone: Yeah, maybe maybe it's a little tricky question, because you're the 1st one or presenting. But I was wondering, you know, when you wrote your proposal. Do you imagine additional capabilities that you would have like to have in this project, and that you could not include, and that you may think, maybe at this stage, that some other of the teams within the center could potentially add. And that you
Angelo Dragone: could that be a project? Is there.
Maurice Garcia-Sciveres: Yeah, certainly. There's there's a lot of things that are, you know.
Maurice Garcia-Sciveres: We don't, you know, haven't done before, and we figure out how to do, and it takes a while. And if someone already has a process for that, it works. But they're typically detailed things. I don't think I have a list handy. But yes, absolutely. I mean, just just like the carbon nanotube aligned carbon nanotube like I said, we went to a vendor. But if someone in the project is doing something like that, we would definitely be very interested.
Maurice Garcia-Sciveres: You know, yeah.
Paul McIntyre: All right. We have a few minutes left. Any
Paul McIntyre: Maybe maybe someone else wants to comment on that. I don't know our channel or.
Maurice Garcia-Sciveres: Or
Maurice Garcia-Sciveres: Francois, you guys are connected. So so again, capabilities that that we would like to make use of, maybe it'd be good to have a list of that, I think. Maybe there was recently we talked about
Maurice Garcia-Sciveres: sputtering of Polymers. What was it? So spider deposition of from methane gas or something like that. Was that Archana? Do you remember.
Archana Raja (Berkeley Lab): Yeah, yeah, actually, just maybe I'll
Archana Raja (Berkeley Lab): So one of the things that we've been trying to do is prepare a real monolayer of quantum dots on different substrates, including
Archana Raja (Berkeley Lab): carbon nanotubes and 2D. Materials. Greg's approach is one way to do it, but for we've also been trying to implement some kind of substrate functionalization. We've had success in the past with polymerizing methane on silicon oxide. But they don't let us do that in our in the in the foundry tool anymore. So in case
Archana Raja (Berkeley Lab): you know, anyone has a tool where we can. I'll, you know, stick a wafer in and polymerize to create. Like, you know, few nanometers of
Archana Raja (Berkeley Lab): polymer is methane on top. We found that to be a very good substrate to create a real monolayer of quantum dots. Usually you can create like an ordered array of like, you know, multi layers of quantum dots.
Archana Raja (Berkeley Lab): or you can create like these individual dots with extremely dilute solutions.
Archana Raja (Berkeley Lab): And the reason we want to create this true monolayers because we're seeing really exciting results in terms of excitation transport being enhanced at that limit, which is, which is to say, it's 1 of the mechanisms in Francois theoretical framework. It's 1 of the tuning knobs, you know. How do you get the information from the light to your Nano hybrid
Archana Raja (Berkeley Lab): center for dissociation, the center of interest and controlling that. So this is just yeah. I can just drop the paper that we've been following that traditionally we could do it at the foundry, but it turned out to be quite a dirty process that was interfering with other more delicate processes that are going on. So if you guys have a janky
Archana Raja (Berkeley Lab): plasma plasma where we can run some methane safely.
Maurice Garcia-Sciveres: I mean, I think it would be very nice to have some sort of a.
Archana Raja (Berkeley Lab): Yeah, like, joint list of capabilities.
Maurice Garcia-Sciveres: Okay. But some way that's some someone in the center can say, Hey, I need this. Does anyone have it? And someone can reply.
Archana Raja (Berkeley Lab): Should we start like a center slack channel or something.
Maurice Garcia-Sciveres: Something. Yeah, we should. We should.
Archana Raja (Berkeley Lab): I know. I think that would be really good, so that ultimately I feel like we should be able to post something in the postdocs or students. I think that's where the real.
Paul McIntyre: Yeah.
Archana Raja (Berkeley Lab): Lubrication needs to happen. So we've been like reaching out to different clean rooms. And I think we found something in Cornell based on Francois Postdoc suggestion. But it's unclear how all of the logistics, ideally, if we could keep it within the center, that would be nice.
Paul McIntyre: That's a great suggestion. Thank you.
Paul McIntyre: Well, we've reached the hour, and we should be respectful of of Everyone's time. Thanks to Maurice and Jackie and the whole team the nanoscale hybrids team for being our lead off presenters. And we'll pick this up again next week and see everyone hopefully, everyone on the call. Maybe a few more people. Then.
Maurice Garcia-Sciveres: Thank you.
Paul McIntyre: Have a good one. Bye.
Grzegorz_Deptuch: Hello!