Oliva Primer-PNNL: Hi Graham!
Grant Johnson: Hey, Oliver.
Oliva Primer-PNNL: Yeah, I'm in the hospital. But I say, oh, I can join waiting for
Oliva Primer-PNNL: to finish the surgery. So I so I'd say I can connect. So you want to present? Yes.
Grant Johnson: I'm gonna start and then chun long. And
Grant Johnson: Zhao, gunner, gonna can you tell me if you can see my slides.
Oliva Primer-PNNL: Of course. Yeah, yes, I can see your slides.
Grant Johnson: It's presentation, mode.
Oliva Primer-PNNL: Yeah, it is in presentation. One.
Grant Johnson: Okay.
Chun-Long Chen (PNNL): Yeah. Now it's good.
Chun-Long Chen (PNNL): Why, I had a another link. I just.
Grant Johnson: No, there were 2 links, so I logged into the one from last week. If no one shows up, I'll I'll switch to the other one.
Chun-Long Chen (PNNL): Okay, yeah, I I clicked the other one there. I was the only one there.
Grant Johnson: Was there? Was there anybody in the other one?
Grant Johnson: No, currently nobody. Is there. Okay?
Chun-Long Chen (PNNL): Yeah, I don't know why the other one is weekly, right, or both weekly, or
Chun-Long Chen (PNNL): actually, I don't know which one joined now.
Grant Johnson: I I did the one that we use last week. So I think that's all right.
Chun-Long Chen (PNNL): Maybe that's the right one.
Chun-Long Chen (PNNL): I think the other one could be
Chun-Long Chen (PNNL): just that used to be a master meeting right for the leadership.
Chun-Long Chen (PNNL): Shall Garcia.
Xiaogan Liang: Hi Eric.
Christian Mailhiot - Sandia National Laboratories: See.
Christian Mailhiot - Sandia National Laboratories: Hi, Jim, how are you doing.
James Ang: Hi, Christian! Well, I'm doing well.
Christian Mailhiot - Sandia National Laboratories: Great.
James Ang: Wow! I feel like I know everyone on the call right now.
Grant Johnson: Right.
Chun-Long Chen (PNNL): I think we usually need to wait for a few minutes right.
James Ang: Oh, yeah, yeah, we'll wait. We'll wait. We'll wait till we get a bigger quorum.
Chun-Long Chen (PNNL): Yeah.
Chun-Long Chen (PNNL): So, Jeff, from your email seems that you still work on your subcontract.
James Ang: Yeah, we don't have any of our subcontracts in place. So I'm I'm I'm requesting a deferral from next week, which.
Chun-Long Chen (PNNL): Oh!
James Ang: Hopefully. That'll be fine.
Chun-Long Chen (PNNL): We had our sub all subcontract, placed like.
Chun-Long Chen (PNNL): I think, at least one month ago.
James Ang: Oh, that's good!
Chun-Long Chen (PNNL): Yeah.
Grant Johnson: University of Washington was actually the slowest.
James Ang: Okay.
Grant Johnson: Sweet enough.
Ting Cao: There was a connection issue. Sorry.
Grant Johnson: 19.
Chun-Long Chen (PNNL): Thank you.
Ting Cao: Hello! How are you?
Chun-Long Chen (PNNL): So who is the organizer? Paul? Is Paul available today?
Grant Johnson: I didn't see an email from him saying he wasn't. He usually did the introductions, if I remember.
Chun-Long Chen (PNNL): Yeah.
Chun-Long Chen (PNNL): wait 5 min. Maybe.
Chun-Long Chen (PNNL): I mean, usually it will be great. It's like like,
Chun-Long Chen (PNNL): like before the presentation. If everybody can see the title.
Grant Johnson: I'm still sharing right. Shanlong.
Chun-Long Chen (PNNL): Yeah, yeah, we can see your 1st line. What I mean is like, the email, right? Kinda like, Oh, we by weekly meeting. We can send the email. Let everybody know who will present, and then we'll
Chun-Long Chen (PNNL): what the topic will be.
Grant Johnson: Yeah, there is a schedule, but it's been revised a few times.
Chun-Long Chen (PNNL): But I don't think that schedule it. Go it go
Chun-Long Chen (PNNL): goes to everyone. At least, I have never seen that schedule.
Chun-Long Chen (PNNL): Oh, no poise here.
James Ang: Let's see, I see Paul is now on.
Chun-Long Chen (PNNL): Yeah, no point is the host.
Chun-Long Chen (PNNL): I think he's yeah. Yeah. Hey, Paul.
Christian Mailhiot - Sandia National Laboratories: Hi Paul.
jsnelso: Hey? Real quick. I think there's some other folks that are on another.
Chun-Long Chen (PNNL): There were 2 link. I think someone need to go there.
Christian Mailhiot - Sandia National Laboratories: This week.
Chun-Long Chen (PNNL): And tell them to join this one.
jsnelso: Yeah, I'll I'll tell them, and I'll be right back.
Chun-Long Chen (PNNL): Okay. Thanks. Yeah.
Grant Johnson: Yeah.
James Ang: I think the reason there are 2 links is, one is
James Ang: for just like the pis and deputies for the meerkat projects.
James Ang: and the other is this larger one which goes to all of the team members.
Chun-Long Chen (PNNL): Okay.
Grant Johnson: So this was the same link from last week, which is why I used it hopefully. This is the right one.
James Ang: I think it's the right one, Grant. It's it's the one with many more attendees.
Grant Johnson: Okay.
Chun-Long Chen (PNNL): Yeah, I think it just happened that one.
James Ang: Good.
Chun-Long Chen (PNNL): Overlap right for this week.
jsnelso: Okay.
Chun-Long Chen (PNNL): At 10.
Chun-Long Chen (PNNL): Do you currently have a student on this project?
Ting Cao: Yes, I'm in the process of locating one.
Chun-Long Chen (PNNL): Okay, yeah. Later, send us the student information.
Ting Cao: Right? So we still need to figure out how much effort the student need to invest because he's currently working also on another project. So I think, since since the grant itself doesn't cover a full student. So then talk to him to understand research, expectation, and the deliverable.
Chun-Long Chen (PNNL): Okay, that's fine. Yeah. I mean, for our bi-weekly meeting, we need a like.
Chun-Long Chen (PNNL): we are still organize this like email list. Yeah.
Ting Cao: Yeah. Great. Thanks.
Grant Johnson: All usually does some introductions or business first, st right before we start.
Chun-Long Chen (PNNL): Yeah, we can wait. I think it probably went to other link.
Angelo Dragone (SLAC): Not sure all is connected. I doesn't seem he was.
Chun-Long Chen (PNNL): Here like a few minutes ago.
Angelo Dragone (SLAC): Oh, okay, maybe to step on.
Chun-Long Chen (PNNL): I think Christian, like Christian, went to check the other link. They will.
Chun-Long Chen (PNNL): Now some people.
Angelo Dragone (SLAC): Oh!
Chun-Long Chen (PNNL): Oh, yeah, crazy. You're back.
Christian Mailhiot - Sandia National Laboratories: Yeah, Jeff. Jeff Nelson went out to
Christian Mailhiot - Sandia National Laboratories: tell the other attendees he should be right back.
Chun-Long Chen (PNNL): Okay.
Chun-Long Chen (PNNL): It's Paul over there.
Angelo Dragone (SLAC): 3, 3 months.
Angelo Dragone (SLAC): Oh, and that is bizarre. We have 2 week, 2 meetings.
Grant Johnson: Yeah, this one's the same link from last week in the previous meetings. And then there's another. There's a new one for today. For some reason.
Angelo Dragone (SLAC): Oh.
Angelo Dragone (SLAC): that's true!
Chun-Long Chen (PNNL): Maybe we should just start.
Grant Johnson: I was gonna wait to see if Jeff came back.
Paul McIntyre’s phone: Yeah.
Angelo Dragone (SLAC): All 10 seconds.
Christian Mailhiot - Sandia National Laboratories: Yeah, what? You go ahead and start.
Grant Johnson: Okay, that's fine. Thank you.
Grant Johnson: So yeah, good afternoon. Everyone. My name is Grant Johnson. I'm a scientist at Pacific, Northwest National Lab.
Grant Johnson: We're still getting some noise there. So if you could mute yourself, please, that'd be great.
Grant Johnson: So I'm pleased today to give you an overview of our microelectronics Science Research Center Project, which is titled Assembly of Molecular Memristors for energy, efficient computing.
Grant Johnson: and the sort of theme of the overall project is captured by this graphics that we just recently had made up where we have molecular memristors in this case, which are actually molecular metal oxides or polyoxymethylates. And then we use biological scaffolds in this case, peptoids to arrange those into device level structures for testing. So
Grant Johnson: 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 Chun Long Chen, who's the project manager for this project here at Pnnl, and he'll tell you more about the Macromolecular assembly. And then Zhaogen Lang will tell you about the device testing that we're doing with these materials.
Grant Johnson: Okay.
Grant Johnson: so I think this audience knows pretty well the goals of neuromorphic computing and the desire to build biologically inspired systems that mimic the brain's parallel event driven and energy efficient information processing. So this is inherently different to sort of Von Neumann computing architectures where you're shuffling information back and forth between computers and memory.
Grant Johnson: And we need materials that basically function as biological synapses. So there's a lot of work that's been done on neuromorphic chips and architectures like these memoristic crossbar arrays.
Grant Johnson: But there are still some material challenges associated with these, and we think that our molecular approach to memristors might have some inherent advantages to address some of these issues. So
Grant Johnson: Memristers play a key role in neuromorphic computing. This is due to their nonlinear dynamics, their multilevel or analog conductance, and the fact that they can be very low power use in certain implementations. They also have the advantages of being non-volatile fast, and again overcoming that von Beneumann bottleneck. So the traditional architecture usually looks something like this. You have your memristive material with a top and bottom electrode.
Grant Johnson: And then the characteristics of this, 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
Grant Johnson: and the market from enristers is predicted to grow dramatically over the coming years. As these things are increasingly used in it, telecoms and in other industries.
Grant Johnson: So, as I mentioned. Despite, you know, all their advantages, there are also some challenges that need to be overcome. One of these clearly is the variability, the device, device and cycle to cycle variability in addition, degradation. And both of these basically relate to the stochastic switching mechanisms that impede reproducible memrist or performance.
Grant Johnson: So on the right hand side. There are just the various mechanisms by which these materials can function. In our case we're concerned with metal oxide. So the 2 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 memristors can be used to modulate both of those effects.
Grant Johnson: So this is kind of a overview figure that summarizes the objectives of our project.
Grant Johnson: We aim to self-assemble molecular memristors with long range order for resilient and energy efficient neuromorphic computing.
Grant Johnson: So you can see there on the left hand side. Hopefully, you can see my laser pointer. We want to take these molecular metal oxides, which are sort of nanometer scale molecular metal oxides. These have multiple electronic charge states or redox states, each of which gives a discrete current voltage response.
Grant Johnson: 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 peptoids which are biomolecular polymers which will allow us to assemble these polyoxomethylates into more ordered structures, creating individual synapses basically for a single polyoxomethylate and then into larger synaptic networks.
Grant Johnson: 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 with conventional Cmos technologies.
Grant Johnson: So the general workflow on the right looks something like this where we design and control these memoristic 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 neuromorphic computing applications.
Grant Johnson: Our project addresses 3 scientific knowledge gaps through 3 integrated research thrusts. The 1st one that's led by myself is focused on the local interactions between these polyoxomethylates and sequence-defined peptoids. And what effect that has on their memoristic states and absorption behavior on 2D interfaces.
Grant Johnson: The second knowledge gap is centered around the effects of peptoid side chain, 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.
Grant Johnson: and the 3rd thrust, led by Zhaogen Lang from University of Michigan, is focused on assembling these materials into devices and actually testing their energy efficiency for computing operations.
Grant Johnson: So here are some images that capture the thrust that I described on the previous slide, we actually take a slightly different approach to many of the people in this center and that we're using solution phase synthesis to make these Polyoxomethylates 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.
Grant Johnson: and then we also connect them through different covalent linkages to peptoids which serve as the host matrix to create an ordered array.
Grant Johnson: So we can do deposition experiments where we create these in very clean environments on 2D interfaces, and we interrogate the current voltage response of individual clusters, using scanning tunneling microscopy and infrared spectroscopy.
Grant Johnson: But then we start to assemble these with peptoids into larger scale arrays which we can then characterize, using larger scale techniques, such as conductive afm and simulation, such as molecular dynamics.
Grant Johnson: The 3rd thrust then involves taking these materials and putting them into both lateral and lateral and vertical meristic configurations
Grant Johnson: and analyzing their current voltage characteristics, the memoristic states and evaluating them for computing applications.
Grant Johnson: So this is just a picture of the team. So this project is led out of Pacific Northwest National Laboratory.
Grant Johnson: I serve as the lead for thrust. One Chan Long Chen leaves the peptoid 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.
Grant Johnson: We've partnered with several other institutes shown here to bring in some additional expertise in theoretical modeling machine learning and device fabrication testing. So we have ting Cao from University of Washington is helping us with our surface simulations. Zhaogen, who I mentioned, is leading our device assembly and testing thrust.
Grant Johnson: 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 for our applications. And then Jan at Berkeley is also helping us with the deposition and device testing.
Grant Johnson: So our 1st thrust again that I lead is focused on the local interactions in the immediate region around these molecular metal oxides.
Grant Johnson: So as you can see here, these clusters are on the order of a half nanometer. In size they have different heteroatoms at the center, things like phosphorus, silicon, and germanium, which can be used to tune the 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.
Grant Johnson: the next step, of course, is then covalently attaching these molecular metal oxides to the polymer, the biopolymer, the peptoid in this case. And we're exploring both Thiol click chemistry and condensation reactions as ways to achieve that.
Grant Johnson: 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.
Grant Johnson: So this allows us to get the sort of individual behavior current voltage response. The memorative states of these polyoxymethylates on surfaces and understand how that's tuned by the parameters that I just described.
Grant Johnson: So 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. 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 actually provide a certain synaptic weight in a neural network.
Grant Johnson: In addition to controlling their electronic conductivity, these polyoxymethylates can also regulate cation, migration and conductive filament formation. 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
Grant Johnson: and polyoxomethylates which have these localized unoccupied molecular orbitals can basically serve as transit corridors for these ions, making those processes much more reproducible.
Grant Johnson: So one technique that we've developed here at our lab 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.
Grant Johnson: 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 and covalent functionalization.
Grant Johnson: 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.
Grant Johnson: and we have some capabilities as well for uhv sample transfer, so these can be moved over to Stm. And Sts. And characterized without exposure to air.
Grant Johnson: So one of the things we think is going to be quite important, for these molecular memoristers are the local interactions between the polyoxymethylate and the peptoid with the support, and then also with adjacent molecular metal oxides.
Grant Johnson: 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, where 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 wave numbers
Grant Johnson: 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.
Grant Johnson: As I mentioned, we are going to study the individual mineristic properties of these epoxy methylates and their assemblies on surfaces, and this is 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.
Grant Johnson: So the project just started this year. So 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 2 different coverages.
Grant Johnson: And then we also have current voltage and Didv curves here, revealing that, in fact, these materials do show multiple discrete conductance states which we believe will be useful for memrist or applications.
Grant Johnson: So I think this is the last slide for my thrust. I just want to emphasize again that the Polyoxomethylates themselves are one memoristic element. But then we need a way to arrange those into macromolecular assemblies with long range order.
Grant Johnson: and to do that we have to covalently attach them to the peptoid. So we're exploring various tethering schemes, using this Tris alkoxyl chemistry shown here, where we can form both amide bonds and also do click chemistry to create strong covalent bonds to the host scaffold.
Grant Johnson: So again, I'm just gonna bring you back to our high level diagram so that
Grant Johnson: summarizes the sort of work that I'm doing on the local interactions of these memristors. 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 peptoid work.
Chun-Long Chen (PNNL): Okay, thanks. Guys.
Chun-Long Chen (PNNL): So as Greg mentioned also in from this overall
Chun-Long Chen (PNNL): picture, right? So this project, what we are really doing is like, we want to take this bow, inspire concept.
Chun-Long Chen (PNNL): as you know, like, from the top of the figure, right? So the the iron transport in the brain. So it's very important for this
Chun-Long Chen (PNNL): energy, efficient neuromorphic computing. So what we try to do for stress 2, right is how we can also create this kind of long range ordering right to tune the Ion transport electron transport. And then we can use a lot of information learned from thrust one. For example, this tunerable memorized state right? How we can organize them into a
Chun-Long Chen (PNNL): long range order. So which kind of show in this cartoon? Right? If we develop this framework structure. And then how we can use this different charge state. And then the ordering of the peptoid to regulate the
Chun-Long Chen (PNNL): iron transport electron transport for building energy efficient device.
Chun-Long Chen (PNNL): Grant, can you? Are you controlling the slide?
Chun-Long Chen (PNNL): Oh, okay, yes. Thanks. So, yeah. Thanks. God.
Chun-Long Chen (PNNL): Like, I mentioned right? This, the thrust. 2. What we really want to do is now say, okay, how we can use the self assembly or the peptide into crystalline material to organize this
Chun-Long Chen (PNNL): memoriesta state material right into this long range ordering. And then to do that, we need to put this memoristic material onto a substrate right which could be the the memoristic electrode, or this semiconductive material.
Chun-Long Chen (PNNL): So we originally put a 4 different way to make this kind of material. One is, for example, if we make this sofa assembled crystal material, we can do this surface agnostic coating.
Chun-Long Chen (PNNL): By doing that we will have layer by layer. This ordered material onto the substrate surface to regulate the iron or electron transport. For example, if we have the solar electrolyzed, this solar ion diffusion through the filament. So then we can use Peptide and pump to control that stage.
Chun-Long Chen (PNNL): And we also, we used to put a like a Pcvd. To do this Pcvd glows of the peptoid material crystal material on the surface. But then, due to the re scope of the work, we kind of like remove the Pcvd from our
Chun-Long Chen (PNNL): task temporary.
Chun-Long Chen (PNNL): So grant, next next slide.
Chun-Long Chen (PNNL): Yeah. So again. So this is kind of repeat what I said. Right? Like, you know, the source 2, it's really say, okay for source one. Once we understand a lot of this electronic structure, and how the local interaction which could be the covalent attachment of the peptoid with the palm or peptoid sequence, right inference, the palm charge state.
Chun-Long Chen (PNNL): Now the question is, how we can use those molecular level understanding. Now build into this crystalline material right to tune the electron and ion transport. So then we can deposit this material onto the
Chun-Long Chen (PNNL): this memory. So like to do to develop this organic inorganic interface interface. Right? So to understand, for example, some of the key question we want to
Chun-Long Chen (PNNL): understand down the road is like how the
Chun-Long Chen (PNNL): peptoid sequence, or the interaction in inter with this inorganic interface, and how we can actually use the inorganic with specific lattice to inference. The ordering of this peptoid pond structure.
Chun-Long Chen (PNNL): And then by doing that, we can, you know, get a lot of fundamental understanding how the ion or the electron flow during the memorized
Chun-Long Chen (PNNL): device and the performance process. And next slide grant.
Chun-Long Chen (PNNL): So this is basically one slice to show.
Niri Govind: What kind of.
Chun-Long Chen (PNNL): How what we had done before. We can actually create a crystalline material right? So the left figure showing, if we keep the same hydrophobic domain. So we can add a lot of different functional group
Chun-Long Chen (PNNL): to do this basically kind of like cold crystallization process. So then, we are able to not only get this crystalline nanomaterial right? So in for this project, we want to develop like palm containing crystalline material, but because of this
Chun-Long Chen (PNNL): high tune ability coming from the peptoid and also coming from this coal crystallization process. So we are able to
Chun-Long Chen (PNNL): tune the
Chun-Long Chen (PNNL): palm local environment which in this last one right? We Grant mentioned, like the local interaction, it's very important to tune in that memorous state. So there you can imagine for this thrust. Once we align those palm in an order, the way. And then we can also tune in the local environment, basically to achieve some kind of like a protein like.
Chun-Long Chen (PNNL): you know, nature system, right? So the local environment is important for iron electron transport.
Chun-Long Chen (PNNL): So the middle figure showing this is actually the Pepto Assembly did on the MOS. 2.
Chun-Long Chen (PNNL): The reason we want to use this is because MOS 2 is also one of the very interesting memoristic material which can, you know, tune the band gap right for building the memoristic device here. What we want to do is we want to use this MOS, 2 kind of temporary, the peptoid assembly. Right? So then, we are able to develop this peptoid palm and the MOS 2 hybrid material. Use this as a
Chun-Long Chen (PNNL): tunable memoristic material for device, and the
Chun-Long Chen (PNNL): right figure is to show. Actually, if we understand the lattice of the inorganic material, we are able to use this inorganic lattice right? Which could be Hopg, like conducting material, or in this middle one, like MOS, 2 semiconductor material. So then, we are able to use this inorganic material
Chun-Long Chen (PNNL): which could be used as memory electrolyte or the buffer material. Right? So now say, okay, use those inorganic lattice to temperate the ordering of this
Chun-Long Chen (PNNL): peptoid palm material which obtained from source one next one.
Chun-Long Chen (PNNL): Yeah. So this is the basically the result we had before to show through the self assembly process. It is, you know, totally doable we can align like we can align this cluster right? In this case we use a palm cluster to show if we have the hydrophobic domain for the tube formation which is on the left figure to show.
Chun-Long Chen (PNNL): and then we can put the palm over there and then to align this into nanotube structure right which we have well aligned this Nano cluster. So in this project we basically use the same approach to align the different palm cluster. So the middle one to show if we change hydrophobic domain. And then we can get this nano sheet. And then the right image basically also show like, we can also put this
Chun-Long Chen (PNNL): cluster in the middle to get this highly crystal material. So this is basically the slides to show through our previous work. We have demonstrated. Align the palm into order. The way to achieve the long range order is totally doable
Chun-Long Chen (PNNL): next slide.
Chun-Long Chen (PNNL): Yeah. So in this slide, like I mentioned, like, we actually originally put 4 different way to make this peptoid pump material onto the inorganic substrate. So the 1st one is doing this solution crystallization which basically we started with amorpho. Right, then let the peptide and the palm to crystallize and then use hydrophobic interaction. Control the crystallization
Chun-Long Chen (PNNL): in the in the solution. So then we can get a lot of this freestanding crystalline a nano sheet.
Chun-Long Chen (PNNL): The second approach is this temporary approach which we can use inorganic substrate as the temporary. The advantage for this second approach is
Chun-Long Chen (PNNL): since later, going to present stress 3 to show they already built some device with MOS 2. And then you can imagine right? Once we have this inorganic memory, and then we can actually use this inorganic surface as the temporary. Further, to grow this
Chun-Long Chen (PNNL): Peptide palm and then use a Peptide palm to further control the iron and the electron transport. And the 3rd one is about building a crystal framework structure. So here we kind of remove the the Pcvd.
Chun-Long Chen (PNNL): And then we will use molecular dynamic simulation to guide us the material synthesis and also go away and don't change from Michigan State. They are using machine learning to kind of like screen the kind of existing like palm crystal and see how we can understand more what kind of interaction, what kind of ordering will be important to build the
Chun-Long Chen (PNNL): the regulated ion and electron transport. And then we also have the in situ Afm, some other characterization to look at. Say, okay, once we build a memoristic device.
Chun-Long Chen (PNNL): And then what kind of surface charge? And then, like, you know, surface potential. Or you know, the bank gap change of the inorganic material will be useful for us to get a certain
Chun-Long Chen (PNNL): performance from the memorista, for example, Xiao Gampo will mention, like, you know, long-term memory, short-term memory, how we can understand more about the you know, material property related to those performance of the memorester
Chun-Long Chen (PNNL): next slides, please.
Chun-Long Chen (PNNL): Yeah. So this is one size to show. For example, right? Once we can build a peptid Nano sheet. So we have this cartoon, which we had a paper last year to show. Like once we have this highly crisp Nano sheets.
Chun-Long Chen (PNNL): Now we want to build the functional right into this nano sheet. One way we can do is you can imagine those kind of ball like right if those are the palm. And now we can use a lot of different chemistry, for example, like those quick chemistry on the right to cover, attach the palm into this sheets forming peptoid. Next slide, please.
Chun-Long Chen (PNNL): Yeah. So this is the result to show. Once we started with, I think here we have 3 different
Chun-Long Chen (PNNL): palm cluster. Right? So that's because they have different chemistry we can use, for, you know, quick chemistry. Or for this.
Chun-Long Chen (PNNL): like am a reaction. So then, basically, once we get this pump containing peptoid, and then we are able to use this for self assembly. And
Chun-Long Chen (PNNL): just me, I think. Can you guys see my slides.
Zbynek Novotny’s iPad: I can't see it.
Chun-Long Chen (PNNL): Oh, okay, I think then maybe it just you know, you cannot see the slides.
Chun-Long Chen (PNNL): Okay, so now we'll just continue. Yeah. So basically, this is to show like, okay, once we have successful covalent attachment right to this as sheets forming peptoid. So then we use Afm Sem to show, indeed, that we have very nice sheets here, and then next slide, please.
Chun-Long Chen (PNNL): and then we also have a
Chun-Long Chen (PNNL): the Tm image to show. Indeed, right? We can see individual like palm cluster around these Nano sheets
Chun-Long Chen (PNNL): next slides.
Chun-Long Chen (PNNL): Yeah, I think now, like I was transfer this to shall gong to tell us how you know we are doing for the device. 4. Star 3.
Xiaogan Liang: Thank and grant. And now talk about the Thrus 3. And so in this direction we push on the device physics. And it's about the memories to device fabrication and the characterization for neuromorphic computing. So as grant and trend long and mentioned advantages using the peptoi and based material to build the memory stores. So here I want to further emphasize.
Xiaogan Liang: and such a device, oriented effort is driven by this hypothesis that
Xiaogan Liang: the long range order in the peptoid nanostructures. And also there's a wider range of tunability of such 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 peptide nanostructures with different the molecular structure, we could build the memory structures
Xiaogan Liang: with lateral or vertical channel configurations, so they they can be programmed
Xiaogan Liang: to have a short term or long-term memory behaviors, or the or the different dynamic and response characteristics.
Xiaogan Liang: And 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 peptoid based memory store. So one of the important feature we want to try is in such a network the interaction among the different synaptic nodes.
Xiaogan Liang: It's driven by the Ionic coupling, and instead of the traditional charge control processes. So this will be very different, traditional. This is Cmos based memorative integrate circuits.
Xiaogan Liang: So, but to achieve such 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 a memory stores that can result in the low energy consumption for operation? Or how do the different peptoid structure
Xiaogan Liang: influence the transport behaviors of the carriers and the ions, 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 next next slides, please.
Xiaogan Liang: So first, st we're going to assemble the materials developed in the Stras, one Thrus, 2 into the memory store device structures and explore how the different macromolecular assembly structure affect the memorative switching behaviors of such device.
Xiaogan Liang: And it's 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 in response to the the spiking signal. So there are a bunch of the
Xiaogan Liang: benchmark testing we're going to perform like a paired pulse of facilitation, long-term potential Asian depression. And more specifically, in this project, we're going to use a peptoid materials to build, for example, the non-volatile memory stores so that memory stores with a long-term memory capability. So we're going to test its performance at a synaptic components in the hardware based
Xiaogan Liang: spiking neural network to see how good this kind of device can handle the spatial temporal data. And also we're gonna make the memory stores with a shorter memory or or the memory stores with very dynamic
Xiaogan Liang: and response characteristics in response to like a spiking signal and some house based the real time signal, and it will give will 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.
Xiaogan Liang: But yeah, thank you.
Xiaogan Liang: And so we're going to use a method like nanofabrication nanodithography to make a nano scale and the memory stores involving the different peptoid nanostructure. And we're going to perform the electronic Ionic transport characterizations in combination with the materials, characterization.
Dong Chen: It is so small.
Xiaogan Liang: We will also build the test band the neural network systems and incorporate the Peptoi memory stores and either testing at the synaptic components or testing at the dynamic memory store to study their response to the real-time signal or spectrum based signal next page, please.
Xiaogan Liang: So in my lab at the University of Michigan, and recently my Phd student established. These are called memory stir, based reservoir computing networks. And right now we are using the 2D layered materials. And in this project we're going to incorporate the Peptide based memory
Xiaogan Liang: and test the performance in the in the neuromorphic computing scenarios. So here the relevant computing is a mathematical framework for building the recurrent neural network
Xiaogan Liang: and and used for parallel computing and control applications. So they take the dynamic signal and without the any the digital electronic process or involvement involvement of the software. And such in such a network can directly generate the the anticipated control signals or some other output
Xiaogan Liang: for the parallel computing applications.
Xiaogan Liang: So traditionally, this kind of the reservoir computing is implemented in the form of the software or the firmware. And but here we, using the memory store to build all hardware based reservoir computing network. So compared with the software
Xiaogan Liang: based rather computing network, such hardware devices can enable much lower energy consumption and a much smaller footprint. So it's a very good for the the system integration.
Xiaogan Liang: And next next slides, please, yeah.
Xiaogan Liang: And Grant, can can you play the left video? So here, I'd like to show you using our current rather computing. And we can demonstrate several different robotic control processes.
Xiaogan Liang: For example, if you have the left video here. And yeah, if you click the, I think, 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 send it to the right of our computing network
Xiaogan Liang: and almost no involvement of the software or digital electronics. So the reservoir computing layer directly generate the control signal to control the movement of the Rover
Xiaogan Liang: and and grant. But please play the yeah. Another one is, my students want to use such
Xiaogan Liang: rather computing layer to control a drone. But it's too challenging. So right now, they decided, just control one arm of the drone. So here you can see
Xiaogan Liang: Here's like a balancing bar using a propeller to control the the position. So during this control process, the position signal, the real tight signal is direct, physically applied to the right of our computing process. The layer and the red of our computing layer, and you
Xiaogan Liang: completely using the physical process to generate the control signal to keep the system in in the balance.
Xiaogan Liang: Grant, can you go back to the previous slides? I want to emphasize one thing.
Xiaogan Liang: Yes, thank you. So so in this project we we the reason we choose. A rather computing network is, if you look at this schematic wheel in, there's a blue color part. There's a rather layer. So it's a very dynamic process to take the input signal. And so here we need a short term memory stores with nonlinear switching behaviors
Xiaogan Liang: and the readout layer the orange part to generate the final output signal. So we need a long-term memory stress with linear switching behavior. So it's a very good scenario. We can test the Peptide based memory stores, because it's this kind of versatile chemistry of the Peptide materials
Xiaogan Liang: give us the opportunity. We can build the short term and the long term memory stores to meet the different and the neuromorphic computing the scenarios
Xiaogan Liang: I saw. There's question from Ryan.
Ryan Coffee’s iPhone (2): Yes, thank you. So my experience before with reservoir compute is for control system and tokamax. And the key was that they used like
Ryan Coffee’s iPhone (2): 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.
Ryan Coffee’s iPhone (2): But is this continuously training itself.
Xiaogan Liang: For our rather computing, we don't need to train the rather layer, so it's a completely rely on the a little bit of randomness of the the devices and.
Ryan Coffee’s iPhone (2): Golf resistances start out random, and then they Brendan
Ryan Coffee’s iPhone (2): in over time. Is that right?
Xiaogan Liang: 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. Yeah. So if
Xiaogan Liang: the the reverse state vector is a diversified enough, so the readout layer can map to what we want for the anticipated control signal. So we need to train the readout layer so that a long-term memory stirs with the linear switching behaviors.
Ryan Coffee’s iPhone (2): Cool. So the the did. You say that the the reservoir is the short term Memristers.
Xiaogan Liang: Okay, good. Cool. No, that's brilliant. It's an autonomous system.
Xiaogan Liang: Yeah, thank you. So we need this a very fast update rate. So we can use for the real time application.
Ryan Coffee’s iPhone (2): Yes, yeah. Okay. I got it. That's wonderful. Thank you.
Xiaogan Liang: Thank you. Yeah. And grant the next slide, please.
Xiaogan Liang: Yeah. Next one.
Xiaogan Liang: Yeah. Right now. You know, we work closely with Pnl, we already got the the Peptide nano nano structure samples from Pnl collaborators like Peptide Nano sheets Nano Rod nanotube. So we
Xiaogan Liang: right now we have been making the memory stores 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 store. We we try to use a single nanotube. But right now our fabrication process is quite a by chance. It's quite random. But it's good enough for individual device fabrication right now. But later, we we're going to propose
Xiaogan Liang: the the research to develop methods. We can generate array of such memory stores.
Xiaogan Liang: Our next slides, please.
Xiaogan Liang: and my students already performed some preliminary characterization, and especially for the nanotube memory stores, and we already see this hysteresis Iv. Characteristics that indicates there is the memorative switching behaviors between the high resistance and low resistance states.
Xiaogan Liang: So here, we said, there's a set compliant current. Limit. So you can see there's abrupt change. We haven't optimized this Iv the voltages, this programming process. We hope we can further make a make it more reliable switching cycles.
Xiaogan Liang: and we also perform this pulse. Programmed characterization. Try to use the the voltage pulses to tune the conductance States. We see the change of the conductance States. But we haven't got very neat this set reset courses. So we're still working on that hopefully. Next time I can show you a much better this. Go up, go down this this kind of pulse program courses
Xiaogan Liang: next slides, please.
Xiaogan Liang: Okay, there's also result from the Lawrence Berkey National Lab. I don't know if the doctor or Dr. John is here like to pre.
Chun-Long Chen (PNNL): No, I think both of them are not here. I mean, I I can also briefly mention this one. It's fine. Yeah.
Xiaogan Liang: Thank you.
Chun-Long Chen (PNNL): Basically, they are making this vertical device. So the result for this slides 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.
Chun-Long Chen (PNNL): because silk have some ordering, and then, when they compared to this amorphous Peptide material, even though both have memory right, the 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 self-assembled crystalline peptide material, and including those
Chun-Long Chen (PNNL): peptor, like the palm containing Peptor sheets, I showed in the source, too. Yeah.
Chun-Long Chen (PNNL): I think next the next slide.
Xiaogan Liang: So in the near future. And we're going to do several things. First, st we further improve the our programming and setting the processes to figure out the the device physics for operating the peptide based memory stores, and we will do more 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.
Xiaogan Liang: And also we want to. I've just mentioned. We want to develop the upscalable approaches that can produce the the larger arrays of a Peptide based memory store, not just random devices on the substrate, and also, like the trend loan and Grant mentioned, and we're going to study the the interface between the peptwise nanostructure and the
Xiaogan Liang: the inorganic two-dimensional layer materials. There could be a new opportunity to generate the memory stores with high uniformity. Next slides, please.
Xiaogan Liang: So this is the approach I mentioned. We propose this Nano and Macro printing process. We hope to produce the the array and of pipe toy based the memory store. So we start with Pdms. Template the protrusive features, define the device array, then, using the shear direction printing approach, we want to selectively deposit
Xiaogan Liang: type 2 nanotube on the protrusive surfaces on the template. So after printing, we can stand out the arrays of such nanotube structure. Finally, after metalization, we can have the larger array for such devices.
Xiaogan Liang: Grant the next page, please.
Xiaogan Liang: and also we will mention that in addition to the memory structures based on the pure peptoy nanostructure, we also want to study the hybrid structure right integrate, and 2D layered materials and the Pipetoi assemblies. So here's 1 example. Right now, we already start to work on that. So recently my students developed this this vertically arranged Bismu cyanide.
Xiaogan Liang: And also we have a modern disulfide based memory stores.
Xiaogan Liang: And such memory store has quite a unique switching behavior. Grant the next page, please.
Xiaogan Liang: So here you can see this is a set reset behavior of our business, this vertically arranged device.
Xiaogan Liang: and using the voltage pulses, we can realize analog conductance tuning. But more important. You can see here, once we stop the application of the voltage pulses, stop the sighting the device going to hold the conductance days for really long time. So that indicates really stable, non-volatile retention of the conductance days.
Xiaogan Liang: And also you. From 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 the such memory structure with a current regulator.
Xiaogan Liang: And, for example, a current limiting transistor to precisely set the conductance days. We want to set the synaptic
Xiaogan Liang: notes value. So for such device, we have a have a potential. We could build the memory, stir network without such current regulator.
Xiaogan Liang: And but right now the challenge is, and such devices still exhibits significant device to what device the the consistency issue. And next page, please.
Xiaogan Liang: So right now, we're doing this. So in up in my lab we deposit the button electrodes
Xiaogan Liang: and deposit business dynamite. We call it half device sample, and we sent such a sample to Pnl, and at the Pnl the the collaborator will deposit a peptwine nanostructures nanotube, nano rod.
Xiaogan Liang: also different palm structures like Grant mentioned, soft landing, the iron soft landing and and Chenlo mentioned the different morphology, and we want to incorporate the long range order in such a structure. So we expect.
Xiaogan Liang: and after fabrication. So when a sample. Go back to my lab. We're going to finish the device structure. So and such device. We expect the incorporation of the pipeline nanostructure can regulate the formation side of the filaments. So we expect to see improve the device to device uniformity consistency. So that's good for our future and the system into the integration. Yeah, so I will stop here.
Chun-Long Chen (PNNL): And how about? Let's just end up here. Let's see if others have question. I know, like we kind of started late.
Chun-Long Chen (PNNL): like any any question for us.
MCINTYRE PAUL: I had a question. Can you hear me.
Chun-Long Chen (PNNL): Hang on!
Chun-Long Chen (PNNL): Yes.
MCINTYRE PAUL: Hi, this is Paul. Sorry I had some audio trouble. So I'm using my phone. Hope you can hear the question. Basically I I was wondering, to what extent are you planning to?
MCINTYRE PAUL: look at the heterogeneity of properties of these structures? I'm reminded for some of the later slides on the electrical characterization and trying to develop more ordered arrays of of wire like
MCINTYRE PAUL: on structures. I'm reminded of carbon nanotube research where there's a pretty lengthy history of characterizing the variability from, you know, properties from one wire to another depending on its structure, or
MCINTYRE PAUL: the way that it's bundled with other wires is. There is that kind of activity planned to sort of sort through these structures, and see how variable their properties are.
Xiaogan Liang: You're
Xiaogan Liang: once we get this array, we want to evaluate the the variation from the device to device. But my understanding, there's a peptoid like the nanotube maybe it's different from the I know the previous. There's a research about assemble the like the carbon nanotube. But but I expect. Maybe the train. Logan? Answer question, how to control the maybe the uniformity of the.
Chun-Long Chen (PNNL): Yeah, the nice thing for this system is like, when we started this peptide nanotube or the nano sheet. We really started with this molecular self assembly. Right? We can precisely control the structure, the crystalline material.
Chun-Long Chen (PNNL): And then we can keep actually the same methodology right? But then tune in the chemistry.
Chun-Long Chen (PNNL): So now you can imagine right if we build a device with with exactly same
Chun-Long Chen (PNNL): kind of tube, but different chemistry.
Chun-Long Chen (PNNL): I believe you know, for from that aspect. Right? We can kind of like
Chun-Long Chen (PNNL): adjust the challenge coming from the carbon nanotube right? Because in the carbon nanotube synthesis there might be some edge
Chun-Long Chen (PNNL): generity over there. But here, like, since we are doing molecular self assembly approach, we know our crystal material system pretty well, and then, once we know the performance from the device. We can actually narrow 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
Chun-Long Chen (PNNL): like the regulation of the Ion transport. Yeah.
Chun-Long Chen (PNNL): And, Paul, did we answer your question?
MCINTYRE PAUL: Yeah, thank, you.
Chun-Long Chen (PNNL): Okay, thanks. Yeah.
Chun-Long Chen (PNNL): I know some of you might need to go right. But if you like to ask other question, you know. Happy to.
Grant Johnson: Honestly don't know.
Grant Johnson: I have to drop off at 4, but you're you and Jogan are welcome to stay and and take further questions.
Chun-Long Chen (PNNL): Okay, and see if there there are any more question. And now we kind of start late. Yeah, so.
Ryan Coffee’s iPhone (2): I have to jump. But thank you very much.
Xiaogan Liang: Okay, thank, you, yeah.
Chun-Long Chen (PNNL): I think if no more question, then we'll just end.
Xiaogan Liang: Yeah.
Chun-Long Chen (PNNL): Okay, thanks. Everyone.
Chun-Long Chen (PNNL): See you next time. Yeah. Bye.
Xiaogan Liang: A good night.