I'm not sure if all is connected, it doesn't seem…. Uh, maybe….
Oh, okay. Maybe, uh, to step out.
He was here, like, a few minutes ago. Uh, I think that, uh, Christian, like, Christian went to check the other link. They were all….
Now, some people, uh, in the other…. Oh, yeah, cruising your back.
Yeah, yeah. Yeah, Jeff… Jeff Nelson went out to, uh.
Tell the other attendees, you should be right back.
Okay. Is Paul over there?
Rewind some tests.
Once we have multiple.
And that is bizarre. We have two meetings.
Yeah, this one, the same length from last week and the previous meetings, and then there's another… there's a new one for today for some reason.
Hang on, maybe we should just start.
I was going to wait to see if Jeff….
That also…. Maybe we could stop.
Okay.
Yeah, why don't you go ahead and start?
Okay, that's fine, thank you. So, yeah, good afternoon, everyone. My name is Grant Johnson. I'm a scientist at Pacific Northwest National Lab.
Um, we're still getting some noise there, so if you could mute yourself, please, that'd be great.
So, um, pleased today to give you an overview of our microelectronics Science Research Center project.
Uh, which is titled, Assembly of Molecular Memoristers for Energy-Efficient Computing.
And the sort of theme of the overall project is captured by this graphics that we just recently had made up.
Uh, where we have molecular memoristers, in this case, which are actually molecular metal oxides, or polyoxymetolates.
And then we use, uh, biological scaffolds, in this case peptides, to arrange those into.
Device-level structures for testing, so…. I'll give kind of an overview of the project, and I'll talk about my thrust, which is focused on sort of local interactions.
I'll then hand it off to Chen Long Chen, who's the project manager for this project here at PNNL.
And he'll tell you more about the macromolecular molecular assembly. And then Zhao Gun Lang will tell you about the device testing that we're doing with these materials.
So, uh, I think this audience pretty would, uh, knows pretty well, uh, the goals of neuromorphic computing, uh, and the, uh, desire to build biologically-inspired systems that mimic the brain's parallel event-driven.
And energy-efficient information processing. Uh, so this is inherently different to, sort of, von Neumann computing architectures, where you're shuffling information back and forth between computers and memory.
And we need materials that basically function as biological synapses. Uh, so there's a lot of work that's been done on neuromorphic chips and architectures like these memoristic crossbar arrays.
But there are still some material challenges associated with these, and we think that our molecular approach.
Two memristers might have some inherent advantages, uh, to address some of these issues.
So, um, Minristers play a key role, uh, in neuromorphic computing. Uh, this is due to their nonlinear dynamics, their multi-level or analog conductance.
And the fact that they can be a very low power use in certain implementations.
Uh, they also have the advantages of being non-volatile, fast. And again, allow… overcoming that von Benoemann bottleneck. So, the traditional architecture usually looks something like this. You have your enristive material.
With a top and a bottom electrode. And then the characteristics of this, uh, the voltage current response.
Looks something like this for a digital response, and like this for an analog response, which is more what we're interested in for these neuromorphic computing applications.
And the market for enristers is predicted to grow dramatically over the coming years, as these things are increasingly used in IT telecoms.
Uh, and in other industries. So, as I mentioned, um, despite, you know, all their advantages, there are also some challenges that need to be overcome.
Uh, one of these, uh, clearly, is the variability, the device-to device, and cycle-to-cycle.
Variability. In addition, degradation. And both of these basically relate to the stochastic switch switching mechanisms that impede reproducible memorister performance.
So, on the right-hand side there are just the various mechanisms by which these materials can function.
Uh, in our case, we're concerned with metal oxide, so the two most important.
Phenomena, of course, are oxygen vacancy, migration, and also conductive filament formation.
And we'll explain a little bit about how our molecular memoristers can be used to modulate both.
Uh, of those effects. So, uh, this is kind of a overview figure that summarizes the objectives of our, um, project.
Uh, we aim to self-assemble molecular membristors with long-range order for resilient and energy-efficient neuromorphic computing.
Um, so you can see there on the left-hand side, uh, hopefully you can see my laser pointer.
Um, we want to take these molecular metal oxides, which are sort of nanometer scale.
Molecular metal oxides. These have multiple, uh, electronic charge states, or redox states, each of which gives a discrete current voltage response.
But we need a way to put those into a material with long-range order that's well-organized on a 2D electrode interface.
So, to do that, we're leveraging peptides, which are biomolecular polymers.
Which will allow us to assemble these polyoxylmetolates into more ordered structures.
Creating individual synapses, basically, for a single polyoxomethylate. And then into larger, uh, synaptic networks.
So, the key games here are controlling that local environment around this molecular metal oxide to give it the current resistance performance that we want.
And then arranging it into longer-range order on electrode materials that are compatible.
Uh, with conventional CMOS technology technologies. So the general workflow on the right looks something like this, where we design and control these memoristive states locally.
We tune this at a larger scale, and then we start going up to signal and memory functionality, and then evaluating these devices for energy efficiency.
And, uh, neuromorphic computing applications. Our project addresses three scientific knowledge gaps, uh, through three integrated research thrusts.
Uh, the first one that's led by myself is focused on the local interactions between these polyoxomethylates and sequence-defined peptides.
And what effect that has on their memoristic states and absorption behavior on 2D interfaces.
The second knowledge gap is centered around the effects of peptide sidechain chemistry, structure, and composition.
And how that can be tuned to control the long-range order of these assemblies.
And also the formation of these nano-confined ion transport channels through which these conductive filaments will form.
And the third thrust, uh, led by Xiaogen Lang from University of Michigan.
Is focused on assembling these materials into devices and actually testing their energy efficiency.
For computing operations. So, uh, here are some images that capture the thrust that I described on the previous slide.
Um, we actually take a slightly different approach to many of the people in this center in that we're using solution-phase synthesis.
To make these polyoxometallates, which are shown on the left-hand side here.
So we can tune what sort of transition metals are in these, the size and charge states of these metal oxides.
And then we also connect them, uh, through different covalent linkages, uh, to polyoxometal… or to, uh, peptides, which serve.
Um, as the host matrix to create an ordered array. So we can do, um….
Deposition experiments, where we create these in very clean environments on 2D interfaces, and we interrogate.
The current voltage response of individual clusters, uh, using scanning tunneling microscopy.
And infrared spectroscopy. But then we start to assemble these with peptides into larger-scale arrays, which we can then characterize using larger-scale techniques, such as conductive AFM.
Uh, and simulations such as molecular dynamics. The third thrust that involves taking these materials and putting them into both lateral and vertical memorister configurations.
And analyzing their current voltage characteristics, the memristive stays, and evaluating them.
For computing applications.
So, uh, this is just a picture of the team. So this project is led out of Pacific Northwest National Laboratory.
I serve as the lead for Thrust One. Chen Long Chen leaves the peptide task.
And then the folks shown on the bottom here bring a whole range of expertise in material synthesis.
Controlled deposition, multimodal characterization, and modeling. And then we have some postdocs, which are going to be joining us shortly at the end of the July and August to push forward.
This effort in the laboratory. Uh, we've partnered, uh, with several other institutes, uh, shown here.
Uh, to bring in some additional expertise and theoretical modeling, machine learning, and device fabrication and testing.
So we have Tin Cow from University of Washington is helping us with our surface simulations.
Xiaogen, who I mentioned, is leading our device assembly and testing thrust.
And then go away, who brings expertise in machine learning models to help us explore the broad parameter space of these materials and select those that are most promising.
Uh, for our applications. And then Jan at Berkeley is also helping us with the deposition and device testing.
So, our first thrust, uh, again, that I lead is focused on the local interactions in the immediate region around.
These molecular metal oxides. So, as you can see here, uh, these clusters are on the order of a half nanometer in size. Uh, they have different heteroatoms at the center, things like phosphorus, silicon, and germanium, which can be used to tune.
The, uh, energy levels of the electronic orbitals. You can also exert a lot of control over those orbitals through the substitution of the transition metals, things like vanadium, molybdenum, and tungsten.
So we will be exploring that through selective substitution reactions. The next step, of course, is then covalently attaching these molecular metal oxides to.
Uh, the polymer, uh, the bio… the biopolymer, the peptide in this case.
And we're exploring both file, click chemistry, and condensation reactions as ways to achieve that.
We have unique capabilities, which I'll tell you a little bit about in a minute, where we can deposit these sort of complex molecules from the gas phase.
Using a technique known as ion soft landing. This allows us to create very well-defined interfaces that are well-suited to characterization.
With spatially resolved techniques, like scanning, tunneling, microscopy, and spectroscopy. So this allows us to get the sort of individual behavior, um, current voltage response.
The memristive states of these polyoxomatylates on surfaces, and understand how that's tuned by the parameters that I just described.
So, uh, here's some evidence of what I was just speaking about. So, if you have one of these molecular metal oxides on a surface.
They can have up to 4 different conductive states, or 4 logic states.
Uh, this is shown here on the right-hand side, again, in these plots.
So, each one of those has the potential to be used to store a certain.
Bit, or to, um, actually provide a certain synaptic weight, uh, in a, uh, neural network.
In addition to controlling their electronic conductivity, these polyoxylmetolates can also regulate cation migration and conductive filament formation.
Um, so as you know, one of the big problems with memristors is this stochastic filament formation, which is often not reproducible cycle to cycle or device to device.
And polyoxymetalates, which have these localized unoccupied molecular orbitals. Can basically serve as transit corridors for these ions, making those processes much more.
Reproducible. So, uh, one technique that we've developed here at our lab, uh, is this technique known as ion soft landing.
And the key advantage here is it allows us to take a mixture containing many complex things, so this is a mass spectrum of these molecular metal oxides.
And if you were to drop cast these or try to solution cast them, you would get everything present in solution.
But with this mass spectrometry approach, we can individually select each of these ions and isolate it on a surface.
And examine the effect of stoichiometry, heteroatom substitution. Uh, and, uh, covalent functionalization.
And then we have some in-situ capabilities to actually examine. The structure of these things on the interface, and how they change when we apply a voltage and change the redox or conductive state.
Of these metal oxides. And we have some capabilities as well for UHV sample transfers, so these can be moved over to STM and STS and characterized without exposure to air.
So, one of the things we think is going to be quite important for these molecular memoristers.
Are the local interactions between the polyoxymetalate and the peptide. Uh, with the support, and then also with adjacent molecular metal oxides.
And one way we can get information about this is by studying the spectroscopy of these things as a function of their surface coverage.
And this is an example here from a paper from a couple years ago.
Or we basically imaged these materials on a surface as a function of surface coverage and showed how the vibrational modes of these things evolve as a function of almost 40 wavenumbers.
From an environment where they're interacting mostly with the electrode surface.
To an environment where they're interacting with each other in a higher coverage regime.
As I mentioned, we are going to study the individual minerista properties of these.
Polyxamethylates and their assemblies on surfaces. And this is… you're done using PNNL's STM and STS capabilities shown here. We have several instruments, both for low temperature and variable temperature measurements.
And this is also coupled with X-ray photoelectron spectroscopy and the ability to dose reactive gases and examine the effect of those environmental conditions.
So, the project, uh, just started this year, um, so, uh, we do have some exciting results to share.
These are some results of these molecular metal oxides deposited onto graphite. These are conductive AFM images shown here.
At two different coverages. And then we also have current voltage and DIDV curves here, revealing that, in fact, these materials do show multiple.
Discrete, uh, conductance states, which we believe will be useful. From Memrist or applications.
So, um, I think this is the last slide for my thrust. Um, I just want to emphasize again that.
The polyoxometylates themselves are one mineristive element, but then we need a way to arrange those into macromolecular assemblies with long-range order.
And to do that, we have to covalently attach them to the peptides. So we're exploring various tethering schemes.
Uh, using this TRIS alkoxyl chemistry shown here. Uh, where we can form both amide bonds and also do click chemistry.
Uh, to create strong covalent bonds to the host scaffold. So, um, again, I'm just going to bring you back to our high-level diagram. So that, um….
Summarizes the sort of work that I'm doing on the local interactions of these memristers. I'm going to hand it off now to my colleague, Chun Long Chen, who's going to tell you more about.
The macromolecular assembly and peptide work.
Okay, thanks, Grant. So, as Greg mentioned, also, uh, in….
From this OO, uh…. Picture, right? So, this project, uh, what we are really, uh, doing is, like, we want to take this BioInspy concept.
As you know, like, from the top of the figure, right? So, uh, the….
Iron transport in the brain, so it's very important for this, uh.
Energy-efficient neuromorphic computing. So what we try to do for SROS2, right, is.
How we can also, uh, create this kind of long-range ordering, right, to….
Tune the iron transport or electron transport. And then we can use a lot of information learned from SRAS-1.
For example, this, uh, tunable…. I'm a Marista state, right? How we can.
Organize them into a…. Long-range order, so which kind of show in this, uh.
Cartoon, right? If we develop this, uh, framework structure. And then how we can use these, uh, different charge state.
And then the ordering of the peptide to legate the. Iron transport, electron transport for building energy-efficient device.
Uh, Grant, can you… are you controlling the slide?
Oh, okay, yes, thanks. So, uh…. Yeah, thanks, Grant. Uh, like I mentioned, right, the SWAS II, what we really want to do is now say, okay, how we can.
Use the self-assembly or the peptor into crystalline material. To organize, uh.
This, uh, memorized the state of material, right, into this long-range ordering, and then.
To do that, we need to put this memorized the material onto a substrate, right?
Which could be the, uh, the memorizedo electrolyte, or this semiconducting material.
So we originally put four different ways to make this kind of material.
One is, for example, if we make this self-assembled crystalline material, we can do this surface agnostic coating.
By doing that, we will have, layer by layer, this ordered material onto the substrate surface.
To, like, really, the ion or electron, uh. Transport, for example, if we have the.
So, uh, electronized, the solar ion. Diffusion through the filament, so then we can use peptide and POM to.
Control that stage. And we also… we used to put, like, a PCVD to do this, uh, PCVD.
Close of the peptide material… crystalline material on the surface. But then, due to the scope of the work, we kind of, like, removed the….
Pcvd from, uh, our…. Task, uh, temporary.
So, Grant, next slide. Yeah, so, again, so this is kind of, uh, repeat what I….
Say, right? Like, you know, this last two, it's really say, okay.
For stress one, once we understand a lot of this electronic structure and how the.
Local interaction, which could be. The covalent attachment of the peptide with the palm.
Or peptide sequence, right, influence the pump charge state. Now the question is, like, how we can use those molecular level understanding.
Now build it into this crystalline material, right, to tune the electron and ion transport.
So then, we can deposit this material onto the. Uh, this, uh, memorizedo electrolyte, so, like, to do, uh, to develop this.
Organic… inorganic interface, right? So, to understand. For example, some of the key questions we want to, uh.
Understand down the road, it's like. How the peptide sequence, or the interaction in… interact with this, uh, inorganic interface.
And how we can actually use the inorganic with specific lattice to influence the.
Ordering of this, uh, peptide pump, uh, structure. And then, by doing that, we can, you know, get a lot of fundamental understanding how the.
Uh, Iron, or the electron. Flow during the Memorista, uh….
Device, uh…. The performance process. And next slide, Grant.
So, this is basically, uh, one slice to show. How, uh, what we had done before.
We can actually create a crystalline material, right? So the, uh, left figure showing.
If we keep the same hydrophobic domain. So we can add a lot of different functional groups.
To do this, basically, kind of, like, co-criticization process. So then we are able to not only get this crystalline nanomaterial, right, so in….
Uh, for this project, we want to. Devote, like, poem containing crystalline material.
But because of this, uh…. High tunability coming from the peptide, and also coming from this co-crystallization process.
So, we are able to…. Tune the POM Local Environment.
Which, in this last one, right, we, uh, Grant mentioned, like, the local interaction is very important to tune in that, uh.
So there you can imagine, for this thrust, once we align those poems in an ordered way.
And then we can also tune in the local environment. Basically to achieve some….
Kind of like a protein, like…. You know, nature system, right? So the local environment is important for.
I and electron transport. So the middle figure showing.
This is actually the Pepto assembly did on the MOS2. The reason we want to use this is because MOS2 is also one of the.
Very interesting memories, the material, which, uh. Can, you know, tune their bandgap, right, for building their memory device. Here, what we want to do is.
We want to use this MOS to kind of temporary the peptide assembly, right? So then we are able to.
Develop this, uh. Heptoid poem and the MOS2 hybrid material use this as a.
Tunable memorized the material for device. And the right figure is to show, actually, if we understand the lattice of the inorganic material.
We are able to use this, uh, inorganic lattice, right, which could be HOPG, like conducting material, or in this middle one, like MOS2 semiconductor material. So then we are able to use this.
Inorganic material. Which could be used as memorized electrolyte, or the buffer material, right? So now, say, okay.
Use those inorganic lattice to temper the. Ordering of this, uh, peptide poem material, which, uh, obtained from, uh, source 1.
Next one. Yeah, so this is the….
Basically, the result we had before to show. Uh, through the self-assembly process, it is, you know, totally doable. We can, uh, align, uh, like, we can align this, uh.
Cluster, right? In this case, we use a POM cluster to show if we have the hydrophobic domain.
Uh, for the tube formation, which is on the…. Left the figure to show.
And then we can put the palm over there, and then to align this.
Into nanotube structure, right, which we have well-aligned this nanocluster. So, in this project, we basically used the same approach to align the, uh.
Different POM cluster. So the middle one to show, if we change hydrophobic domain.
And then we can get these nanosheath. And then the right image basically also show, like, we can also put this, uh.
Cluster in the middle to get this highly crystalline material. So this is basically the slides to show.
Uh, through our previous work. We have demonstrated that align the poem into an ordered way to achieve the long-range order is.
Totally doable. Next slide. Yeah, so, uh, in this, uh, SWAT, like I mentioned, like, uh, we actually originally put four different ways to make a….
This peptidipone material onto the inorganic substrate. So the first one is doing this solution crystallization, which, basically, we started with amorpher, right, then let the peptide and the.
A pump to crystallize, and then use hydrophobia interaction control the crystallization.
In the solution, so then we can get a lot of these freestanding crystalline.
At NanoSheets. The second approach is this temporary approach, which we can use inorganic substrate as the temporary.
The advantage for this second approach is. Uh, since, uh, yeah, Shogun later going to present Strast 3 to show.
Uh, they already built some device with MOS2. And then you can imagine, like, once we have this inorganic memorized divide, and then we can actually use this inorganic surface as the temporary further to grow this, uh.
Pept to a palm, and then use, uh. Compared to a point to further control their.
The IR and the electron transport. And the third one is about building a Christian framework structure. So here, we kind of remove the….
Uh, the PCVD, uh…. Admit that. And then, uh, we will use molecular dynamical simulation to.
Guide us the material synthesis, and also, uh. Go away and don't change from, uh.
Michigan State, they are using machine learning to kind of, like, screen.
They're kinda existing, like, palm crystal. And to see how we can understand more what kind of interaction, what kind of ordering will be important.
To build the, uh…. The laggerated ion and electron transport.
And then we also have the in-situ AFM, some other categorization to.
Look and say, okay, once we build that memorized device. And then what kind of surface charge, and then, like, a, you know, surface potential, or, you know, the bandgap change of the inorganic material will be useful.
For us to get a certain, uh. Performance from the memorister. For example, Xiao Ganpo will mention, like.
You know, long-term memory, short-term memory, how we can understand more about the.
You know, material, uh, property related to those, uh, performance of the memorization.
Next slide, please. Yeah, so this is one slice to show, for example, right, once we can build a peptide nanosheet.
So we have this cartoon, which we had a paper last year to show, like, once we have this.
Highly Chris and nanoSheets. Now we want to build the, uh, functional, right, into this nanosheet. One way we can do is.
You can imagine those kind of balls, like, right? If those are the poems.
And now we can use a lot of different chemistry. For example, like those, uh, click chemistry on the right.
To covertly attach the palm into this, uh, sheets, uh, forming peptide. Next slide, please.
Yeah, so this is…. The, uh, result to show, once we started with, uh, I think here we have 3 different, uh.
Palm Cruster, right? So that's because they have different chemistry we can use.
For, you know, Creek Chemistry, or for, uh. This, uh… I'm a reaction, so then, basically, once we get this, uh.
Palm containing peptide, and then we are able to use this for self-assembly, and….
I think…. Can you guys see my slides?
I couldn't see it.
Oh, okay, I think then maybe it just…. You know, because someone mentioned you cannot see the slides.
It was a mistake.
Okay, so that would just continue, yeah. So basically, this is to show, like, okay, once we have successful covalent attachment, right, to this, uh.
Sheets forming peptide, so then we use AFM and SEM to show, indeed, that we have.
Very nice sheets here. And then, next slide, please.
And then we also have, uh, the TM image to show indeed, right, we can see individual.
Like, palm cluster around these, uh, nanosheets. Next slide.
Yeah, I think now, like, I was, uh…. Transfer this to, uh, Shaogang to tell us how.
You know, we are doing for the device for Star 3.
Sanctuin Loan and Grant. And I'll talk about the Thrust 3, and so in this direction, we push on the device Felix.
And it's about a memory-restricted device fabrication and characterization. For neuromorphic computing. So, as Grand and Chen Long mentioned advantages using.
The PEP2I and base material to build the memoristers. So here, I want to further emphasize.
And such a device-oriented effort is driven by this hypothesis that.
The long range order in the peptide nanostructures, and also there's a wider range of, uh, tunability.
Of such a macromolecular assemblies. So, can provide the new method for us to create, we call this a biorealistic.
Synaptic devices. So, for example, and using the different PEP2 nanostructures with different molecular structure, we could build the memory serves.
With lateral or vertical channel configurations. So they can be programmed.
To have a short-term or a long-term memory behaviors? What are the different dynamic and the response.
Characteristics. Um, so they can meet the different neuromorphic compute… computing scenarios. Also, in this project, we hope to build the.
Neuromorphic computing network system using such peptide-based memory strter. So, one of the important features we want to try is, in such a network.
The interaction among the different synaptic nodes is driven by the ionic coupling, and instead of the traditional.
Charger control processes. So this will be very different traditional… there's a similar space, uh.
Memoristive integrated circuits. So, but to achieve such a goal. We need to address the important knowledge gap. The key question is.
What kind of peptide nanostructures or assemblies that can be really used to make memory stirs?
That can result in the low energy consumption for operation. Or.
Uh, how do the different peptide, uh, structure. Influence the transport behaviors of the carriers and the.
Ayers, and the result in the low energy consumption. So, to address such question, we have a bunch of the research activity proposed.
I'll grant the next slide, please. So, first of all, we're gonna assemble the materials developed in the CRAS-1, the Thras 2.
Into the memory stored device structure, and explore. How the different macromolecular assembly structure affect the.
Ma'am resistive switching behaviors of such device. And we're gonna… using the voltage program and the pulse program method.
To study the characteristics of such device and evaluate the device consumption.
And also, we will… we will study the dynamic characteristics of the such devices.
Your response to the spiking signal. So there are a bunch of the.
Benchmark testing we're gonna perform, like a paired policy. Long-term potential agent, depression.
And more specifically, in this project, we're gonna use a pep toy.
Materials to build, uh, for example, the. The non-volatile memory stirs. So that's memory stirs with a long-term.
Memory capability. So we're gonna test its performance at a synaptic.
Components in the hardware-based. It's the biking neural network.
To see how good this kind of device can handle the spatial, temporal data.
And also, we're gonna make the memory stirs with, uh. Short-term memory, or the memory starts with very dynamic.
And response characteristics, in response to, like, spiking signal. And, um, house-based, uh, real-time signal, and we'll be tested for processing the.
The spatial-temporal information. Carried by the real-time analog signal. So this is important for the edge computing and robotic control application.
Grand Next Place, next…. But yeah, thank you. And so we're going to use a method like nanofabrication, nanolithography, to make a nanosca.
And the memory stirs involving the different peptide nanostructure, and we're going to perform the electronic ionic transport.
Characterizations in combination with the materials characterizations.
Why it is so small.
Oh, we will also build the test stand, uh, the neural network systems.
And he incorporated the Peptoi memory stirs. And either testing at a synaptic component, or.
Testing as dynamic memory structure to study their response to the real-time signal or spanking-based signal.
Uh, next page, please. So in my lab at the University of Michigan.
And recently, uh, my PhD student established this, uh, called Memory Store-based.
Rather computing networks. And right now, we are using the 2D layered materials, and in this project, we're gonna….
Incorporate the peptide-based memory registers. And to test the performance in the….
In the neuromorphic computing scenarios. So here, the red of our computing is a mathematical framework.
For building the recurrent neural network. And used for parallel computing and control applications.
So they take the dynamic signal, and without the… any, uh, the digital electronic process or involvement of the software.
And such a network can directly generate to the anticipated control signals or some other output.
For the, uh, parallel computing applications. So, traditionally, this kind of, uh, the red-work computing is implemented.
In the form of the software, or the firmware. And… but here, we're using the memories to build all hardware-based radar computing network.
So compared with a soft tool. Software-based radar computing network.
Such a hardware devices can enable much lower energy consumption. And a much smaller footprint. So it's very good for the….
The system integration. And, uh, next… next slide… slides, please.
And Grant, can you play the left video? So here, I'd like to show you, using our current radar computing.
And we can demonstrate, uh, several, uh, different robotic control. Uh, control, uh, processes.
For example, if the left video here, and yeah, if you click the….
I assume, yeah, here. So this is a target tracking. And navigation of a robotic rover. So during this process, you see the position.
Signal of the target is directly sent to the right of our computing network.
And almost no involvement of the software or digital electronics. So the rest of our computing layer directly.
Generate the control signal. To control the movement of the rover.
And Grandpa plays, uh. Later, yeah. Another one is my students want to use such.
Rather computing, uh, layer to control a drone, but it's too challenging, so right now they decided to just control one arm of the drone.
So, here you can see, um. Here to, like, a balancing bar using a propeller to control the position.
So during this control process, the partition signal, the real-time signal, is direct, physically applied to the right of our computing process… the layer.
And the regular computing layer, and you. Completely yielding the physical process.
To generate the control signal, to keep the system in the balance.
Uh, Grant, can you go back to the previous slide? I want to emphasize one thing.
Yes, thank you. So… so in this project, we… we… the reason we choose a random computing network is.
If you look at this schematic view in the blue color part, there's a reservoir layer, so it's a very dynamic process.
To take the input signal. And so here, we need a short-term memory source with nonlinear switching behaviors.
And the readout layer, the orange part, to generate the final output signal.
So we need a long-term memory stirs with linear switching behavior. So it's a very good scenario. We can test the.
Peptide-based memory registers. Because it's this kind of versatile chemistry of the peptide materials.
Give us the opportunity, we can build the short-term and the long-term memory registers.
To me, to the different, uh, the neuromorphic computing, uh, the scenarios.
I saw there's a question from Ryan.
Yes, thank you. So, my experience before with reservoir compute is for a control system and tokamax.
And the key was that they used, like, um…. Training, like, a lot of training to try and get the weights right and the connections.
And training ended up being, like, a genetic algorithm for the random connectivity.
But is this continuously training itself?
And for our, right, we're computing, we don't need to train the radar layer.
So it's a completely rely on the… a little bit of randomness of the devices.
And…. Yeah, it's random.
Yeah. So, like, all the resistances start out random, and then they burn in over time, is that right?
Yeah, they just needed a nonlinear response behavior that can. Can map the kinetic information carried by the input signal to a high-dimensional data space. That's good enough.
Yep. Yep.
So, if the reservoir state vector is diversified enough. So the readout layer can map to what we want for the anticipated control signal. So we only need to.
I see.
Train the readout layer, so that's a long-term memory source with a linear switching behaviors.
Ah, cool. So the… did you say that the reservoir is the short-term memristers?
Okay, good, cool. No, that's brilliant. It's an autonomous system.
Exactly, yes. We need a more time. Yeah, thank you. So, we needed this very fast update rate, so we can use for the real-time application.
Yes. Yeah, okay, I got it. That's wonderful, thank you.
Thank you. Yeah. Grant, next slide, please.
Yeah, next one.
Yeah, right now, um, your… we work closely with PNNL. We are the garden, the Peptide.
Nano, uh, nanostructure samples from PNRL collaborators. Like a peptide nanosheath, nanorod, nanotube, so we….
Right now, we have been making the memories using such material. So here, I show you the just example.
Of the memory stores using the multi-nanotube channel, and also here you can see there's a memory steroid.
We try to use a single nanotube. But right now, our fabrication process is quite a….
Uh, by chance, it's quite random, but it's good enough. For individual device fabrication right now, but later we're gonna propose.
The research to develop methods, we can generate an array of such.
Memory is jerse. Uh, next slide, please. And my student already performed some preliminary characterization, and especially.
For the nanotube memory stirs. And we already see this hysteresis, IV characteristics that indicates.
There's the memory still switching behaviors between the high resistance and low resistance states.
So here, we said that there's a set compliant current limit, so you can see there's a rough change.
Um, we haven't optimized this IV voltage, uh, this programming process.
We hope we can further make a… make it a more reliable switching cycles.
And we also performed this pulse program. Characterization, try to use the.
The voltage pulses to tune the conductant states. We see the change of the conductant states, but.
We haven't got a very neat. This asset recites courses.
Um, so we're still working on that. Hopefully, next time, I can show you a much better.
Let's go up, go down, this kind of pulse program courses.
A nexus-wise place. Okay, there's also a result from the Lawrence Berkey Natural Lab.
Um, I don't know if the Dr. Hsu or Dr. John is here like to present.
No, I think both of them are not here. I mean, I can also briefly mention this one, it's fine, yeah.
Thank you. Yeah, good luck.
So here, basically. Uh, they're making, uh, this vertical device.
So the result for this slide is to show. Even though, like, on the left, right, it's a silk, I mean, for this project, we don't really use silk.
But this is a proof of concept to show. Because Silk has some ordering, and then when they compare to this amorphous peptide material.
Even though both have memorized, uh. Behavior right there, ordering actually can make the performance much better, which is….
As we expected, because. Now we are going to use this capability to start to measure more this, uh.
Self-assembled crystalline peptide material, including those. Peptide, like, the palm containing peptides I showed in the SRS2, yeah.
I think the next slide.
Yes, that's true. So, in the near future, and we're gonna do several things. First, we further improve the.
Our programming and the setting, uh, the processes. To figure out the device physics for operating the peptide-based memory strips.
And we will do more of the material characterization, and especially we want to visualize the kinetic… the behaviors of ions.
And… or the formation of the ionic filaments in the peptide nanotube.
And also, we want to… I've just mentioned, we want to develop the upscalable approaches.
That can produce the larger arrays of peptide-based memories, not just random devices on the substrate.
And also, like, the trend line and Grant mentioned. And we're gonna start the interface between the peptide nanostructure and the.
You know, organic two-dimensional layer materials. Um, there could be a new opportunity to generate the memory stores with high uniformity.
Next slide, please. So this is the approach I've mentioned we proposed this nano and macro printing process.
We hope to produce the array. And of peptide-based memorister. So we start with, uh, PDMS template.
The protrusive features define the device array. Then using the shear direction printing approach.
We want to selectively deposit. Type to a nanotube on the protrusive surfaces, on the.
The template. So after printing. We can stand out the arrays of such nanotube structure. Finally.
After metalization, we can. Have the larger array of such devices.
I'll grant the next page, please. And also, uh, we will….
I mentioned, in addition to the memory stirs. Uh, based on the pure peptide nanostructure, we also want to study the.
The hybrid structure, right, integrate. And 2D layer the materials and the pipe toy assemblies.
So here's one example. Right now, we already start to work on that.
So, recently, my students developed this… this vertically arranged bismos cyanide.
And also, we have a modern disulfide-based memories. And such a memory store has quite a unique switching behavior. Uh, grant the next page, please.
So, here you can see, this is a set reset behavior of our bismos cyanide memorister, this vertically arranged device.
And using the voltage pulses. We can realize anal.
Conductance tuning. But more important, you can see here, once we stop.
The application of the voltage pulses. Stop the sighting. The device is going to hold the conductance days.
For a really long time. So that indicate really stable non-volatile retention of the conductance days.
And also, you found this result, you can see there's almost no post-setting relaxation, because many.
Reported analog memory stores after setting, there is a significant relaxation of the.
The conductance. And so sometimes people need to integrate such memory store.
With a current regulator. For example, our current limiting transistor to precisely set the.
Conductance days we want to set a synaptic. Notes value. So, for such device, we have a… have a potential… we could build the….
The memory store network. Without such current regulator.
And… but right now, the challenge is…. And such devices still exhibits significant device-to-device.
The consistency, uh, issue. Next page, please.
So, right now, we're doing this. So in my lab.
We deposit the button electrodes. And deposited bitum and stylinite, we called it half-device sample, and we sent.
Such a sample to PNL. Um, at the P&L, um….
The collaborator will deposit a PEP2 nanostructures, nanotube, nanorod. Also different poem structures, like Grant mentioned, soft landing, the iron soft landing.
And Trend Long mentioned different morphology. And we want to incorporate the long-range order in such a structure.
So we expect, and after fabrication, so when the sample go back to my lab.
We're gonna finish the device structure. So, in search device, we expect the incorporation of the PEP2 nanostructure.
Can regulate the formation site of the filament. So, we expect to see.
Uh, you improve the device-to-device, uh, uniformity consistency, so that's good for our future.
And the system, the integration. Yeah. So, uh, I will stop here, and….
Grant, how about… let's just end up here. Let's see if others have questions. I know, like, we kind of started late.
Like, any… any question for us?
Yes.
I had a question, can you hear me? Hi, this is Paul. Um, sorry, I had some audio troubles, so I'm using my phone.
Uh, hope you can hear the question. Basically, I was wondering, um.
To what extent are you planning to, um. Look at the heterogeneity of properties of these.
Structures. Um, you know, they… I'm reminded, for some of the, um, later slides on.
The electrical characterization and trying to, uh. Develop more ordered, um, arrays of, um.
Of wire-like, uh…. Uh… bomb structures.
I'm reminded of carbon nanotube research, where. There's a pretty, uh, lengthy history of characterizing the variability.
From, you know, properties from one wire to another, depending on its structure, or….
Uh, the way that it's bundled with other wires. Um, is there… is that kind of activity planned to sort of sort through these structures and see how variable their properties are?
Yeah, once we get into this area we want to, uh, evaluate the variation from the device to device.
Um, but my understanding, there's a peptide, like the nanotube, um, maybe it's different from the.
I know the previous… there's research about assemble the…. Uh, like a cargo nanotube, but.
Um, but I expect maybe the trend load can answer questions of how to control the, maybe the uniformity of the….
Yeah, the nice thing for this system is. Right? Like, when we started this peptide nanotube, or the nanosheet, we really started with, uh.
This molecule self-assembly, right, we can…. Precisely control the structure, the crystalline material.
And then we can keep, actually, the same, uh…. Morphology, right? But then tuning their chemistry.
So now you can imagine, right, if we build a device with exactly the same.
Kind of two, but different chemistry. I believe, you know, from that aspect, right, we can kind of, like.
Adjust the challenge coming from the carbon nanotube, right? Because in the carbon nanotube synthesis, there might be some hedge….
Generally over there, but here, like, since we are doing molecular self-assembly approach.
We know our, uh, crystal material system pretty well. And then once we know the performance from the device, we can actually.
Narrowed down to specific tube, right, and then just keep the same….
Methodology and change the chemistry, see how the. Chemistry, as we kind of proposed, you know, to tune in the….
Like, the decoration of the ion transport, yeah. I don't know, Paul, did we answer your question?
Okay, thank you. Yeah, thank you.
Okay, thanks, yeah. I know some of you might need to go, right? But if you….
I'd like to ask other questions, you know, happy to…. Honestly, uh, yeah.
Look, I have to drop off at 4, but you and Zhao Gunn are welcome to stay and take further questions.
Okay, see if there are any more questions. And then we kind of start late, yeah, so….
I have to jump, but thank you very much.
Okay, thank you. Yeah. I think if no more questions, then we would just end.
Thank you.
Yeah. Okay, thanks everyone.
Yeah.
See you