YouSaid · the spoken record

Jeffrey Shainline

lines on the record: 208
first: 2021-09-26
most recent: 2021-09-26
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Especially at low light levels, if you're in a medium and you have a bright high light level, you can get them to interact with each other through the interaction with that medium that they're in. But that's a little bit more exotic. And for the purposes of this conversation, we can assume that photons don't interact with each other. So if you have a bunch of them all propagating in the same direction, they don't interfere with each other. If I want to send”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, this is. A lot that goes into it, I guess, but just try to speak to the simplest part of it. Electrons interact strongly with one another. They're charged particles. So if I pile a bunch of them over here, they're feeling a certain amount of force and they want to move somewhere else. They're strongly interactive. You can also get them to sit still. An electron has a mass. So you can cause it to be spatially localized. So for computation, that's useful because now I can make these little devices that put a bunch of electrons over here and then I change the state of a gate like I've been describing put a different voltage on this gate and now I move the electrons over here. Now they're sitting somewhere else. I have a physical mechanism with which I can represent information. It's spatially localized and I have knobs that I can adjust to change where those electrons are or what they're doing. Light by contrast photons of light which are the discrete packets of”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Let me say one more thing just to clarify communication. Ideally, does not change the information. It moves it from one place to another, but it is preserved”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Rolf Landauer, not to be confused with Lev Landau. Yeah, and he made huge contributions to our understanding of the reversibility of information and this concept that energy has to be dissipated in computing when the computation is irreversible, but if you can manage to make it reversible, then you don't need to expend energy. But if you do expend energy to perform a computation, there's sort of a minimal amount that you have to do. And it's KT log too”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Taken in information and output different information. Hopefully. Reducing the total amount of information and extracting what's useful. Communication is then getting that information from the location to which it's stored because information is physical as Landauer emphasized. And so it is in one place. And you need to get that information to another place so that something else can use it for whatever computation it's working on. Maybe it's part of the same network and you're all trying to solve the same problem, but neuron A over here just Deduced something based on its inputs, and it's now sending that information across the network to another location. So that would be the act of communication.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Necessarily, it could be that could be how your system is performing the computation, or it could be asynchronous. There are lots of ways to find the key. It depends on the nature of the data, it depends on that's a very simplified example, a picture with a key in it. What about if you're in the world and you're trying to decide the best way to live your life?”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The input is that entire picture and the output might be the coordinates where the key is. So you've reduced the total amount of information you have, but you found the useful information for you in that present moment. That's the useful information.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, just to first speak to computation versus communication, I would say computation is essentially taking in some information. Performing operations on that information and producing new, hopefully more useful information. So for example, imagine you have a picture in front of you and there is a key in it and that's what you're looking for for whatever reason. You want to find the key. We all want to find the key.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Processors in and of themselves that can take in many different kinds of inputs on many different spatial and temporal scales and produce many different kinds of outputs so that they can perform different computations in different contexts.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Sure, no Right. So I would say to think of a transistor as the building block of a digital computer is accurate, you use a few transistors to make your logic gates, you build up more processors from logic gates and things like that. So you can think of a transistor as a fundamental building block, or you can think of as we get into more highly parallelized architectures, you can think of a processor as a fundamental building block to make the analogy to the neuro side of things. A neuron is not a transistor. A neuron is a processor. It has synapses, even synapses are not transistors, but they are more, they're lower on the information processing hierarchy in a sense. They do a bulk of the computation, but neurons are entirely”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“By and large, the computation proceeds in a serial manner. It's not that way in the brain. In the brain, you're always drawing information from different places. It's much more network-based computing. Neurons don't wait for their turn. They fire when they're ready to fire. And so it's asynchronous. So one of the other things about a digital system is you're performing these operations on a clock. And that's a crucial aspect of it. Get rid of a clock in a digital system. Nothing makes sense anymore. The brain has no clock. It builds its own timescales based on its internal activity.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Computing. Also in conventional digital computers or digital computers in general, you have a concept of what's called arithmetic depth, which is jargon that basically means how many sequential operations are performed to turn an input into an output. And those kinds of computations in digital systems are highly serial. meaning that data streams, they don't branch off too far to the side. You do. You have to”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, all right, so maybe we should disambiguate there are a variety of different kinds of computation. I don't pretend to be an expert in the theory of computation or anything like that. I guess it's important to differentiate, though, between digital logic, which represents information as a series of bits, binary digits, which, you know, you can think of them as zeros and ones or whatever. Usually they correspond to a physical system that has two very well separated states and then other kinds of computation, like we'll get into more the way your brain works, which it is, I think, indisputably processing information, but where the computation begins and ends is not anywhere near as well defined. It doesn't depend on these two levels. Here's a zero, here's a one. There's a lot of gray area that's usually referred to as analyze.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“But building a digital computer is a lot more than just that elemental operation. It's everything that goes into it, including the manufacturing, including the packaging, including the various materials aspects of things. And even in some of those early papers, I can't remember which one it was, Lickoriff said something along the lines of you can see how we could build an entire family of digital electronic circuits based on these components. They could go hundred or more times faster than semiconductor logic gates. But I don't think that's the right way to use superconducting electronic circuits. He didn't say what the right way was, but he basically said digital logic trying to steal the show from silicon is probably not what these circuits are most suited to accomplish.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I wasn't working in the field at that time, but later when I went back and read the literature, I was just like, wow, this is so awesome. And so you might think, well, the reason why it didn't displace silicon is because silicon already had so much momentum at that time. But that was the 90s. Silicon kept that momentum because it had the simple way to keep getting better. You just make features smaller and smaller. You know, it would have to be I don't think it would have to be that much better than silicon to displace it, but the problem is it's just not better than silicon. It might be better than silicon in one metric speed of a switching operation or power consumption of a switching operation.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That's an excellent question. That's an excellent way to frame it. And, you know. I don't know the answer to that, but what I think is okay, so the history of the superconducting logic goes back to the 70s IBM made a big push to do superconducting digital computing in the 70s, and they made some choices about their devices and their architectures and things that in hindsight were kind of doomed to fail. And I don't mean any disrespect for the people that did it. It was hard to see at the time. But then another generation of superconducting logic was introduced, I want to say the 90s someone named Lickarev and Seminov, they propose an entire family of circuits based on Joseph's injunctions that are doing digital computing based on logic gates and or not these kinds of things. And they showed how it could go hundreds of times faster than Silicon Microelectronics. And it's extremely...”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's not going to happen. I don't think superconductors are going to replace semiconductors for digital computation. There are a lot of reasons for that, but I think ultimately what it comes down to is all things considered cooling errors, scaling down to feature sizes, all that stuff, semiconductors work better at the system level.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, large systems. So then you can contrast what's going on in your cell phone with what's going on at one of the supercomputers colleague Katie Schumann invited us out to Oak Ridge a few years ago so we got to see Titan. That was when they were building summit. So these are some high performance supercomputers out in Tennessee. And those are filling entire rooms the size of warehouses, you know. Once you're at that level, okay, there you're already putting a lot of power into cooling. You need cooling is part of your engineering task that you have to deal with. So there it's not entirely obvious that cooling to four Kelvin is out of the question. It has not happened yet, and I can speak to why that is in the digital domain if you're interested.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Cooling system can achieve Kelvin. Exactly. For Kelvin, you need liquid helium. And so liquid helium is expensive. It's inconvenient. You need a cryostat that sits there. And the energy consumption of that cryostat is impracticable for it's not going in your cell phone. So you can picture holding your cell phone like this. And then something the size of, you know, A keg of beer or something on your back to cool it, like that makes no sense. So, if you're trying to make this in consumer devices, electronics that are ubiquitous across society, superconductors are not in the race for that.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, yes. So let's dissect a couple of those different things. The super cold part, let me just mention for your gamers out there that are trying to clock it at four gigahertz and would love to go to”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Field, it depends on an interplay between the junction and that loop. And you can't make that loop much smaller. And it's not for practical reasons that have to do with lithography. It's for fundamental physical reasons about the way the magnetic field interacts with that superconducting material. There are physical limits that no matter how good our technology got, those circuits would, I think,”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, the physics is understood well. The physics of Joseph's injunctions is understood well. The technology is understood quite well, too. The reasons why it hasn't displaced silicon microelectronics in conventional digital computing, I think are more related to what I was alluding to before about the myriad practical, almost mundane aspects of silicon that make it so useful. You can make a transistor ever smaller and smaller and it will still perform its digital function quite well. The same is not true of a Josephus injunction. You really, they just, it's not the same thing that there's this feature that you can keep making smaller and smaller and it'll keep performing the same operations. This loop I described, any Josephin circuit, well, I want to be careful. I shouldn't say any Josephson circuit, but many Josephson circuits, the way they process information or the way they perform whatever function it is they're trying to do, maybe it's sensing a weak magnetic.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the speed of the pack is actually these fluxons of current that are generated by Joseph's injunctions, they can actually propagate very close to the speed of light, maybe something like a third of the speed of light. That's quite fast. So one of the reasons why Joseph's injunctions are appealing is because their signals can propagate quite fast and they can also switch very fast. What I mean by switch is perform that operation that I described where you add current to the loop, that can happen within a few tens of picoseconds. So you can get devices that operate in the hundreds of gigahertz range. And by comparison, most processors in our conventional computers operate closer to the one gigahertz range, maybe three gigahertz seems to be kind of where those speed. Have leveled out.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Flexon is one of these quantized sort of amounts of current that you can add to a loop. And this is a cartoon picture, but I think it's sufficient for our purposes.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“It can carry this supercurrent that I've described, this current that can propagate without dissipation, up to a certain level. And if you try and pass more current than that through the material, it's going to become a resistive material, a normal material. So in the Josephson junction, the same thing happens. I can bias it above its critical current. And then what it's going to do, it's going to add a quantized amount of current into that loop. And what I mean by quantized is it's going to come in discrete packets with a well-defined value of current. So in the vernacular of some people working in this community, you would say you pop a flux on into the loop. So a flux on.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, exactly. So, how do you change state? Now, picture if I have a current bias coming down this line of my circuit and there's a Joseph's injunction right in the middle of it. And now I make another wire that goes around the Joseph's injunction. So I have a loop here, a superconducting loop. I can add current to that loop by exceeding the critical current of that Josephus injunction. So like any superconducting material,”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Through the entire circuit, so you can imagine suppose you had a loop setup that had one of those weak links in the loop. Current would flow in that loop even if you hadn't applied a voltage to it. And that's called the Josison effect. So the fact that there's this phase difference in the quantum wave function from one side of the tunneling barrier to the other induces current to flow.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Superconducting state on one side and on the other side, and that the superconducting wave function actually tunnels across that gap. And when you create such a physical entity, it has very unusual current voltage characteristics.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“They can service gates. So I'm not sure how concerned to be with semantics, but let me just briefly say what did Joseph's injunction is, and we can talk about different ways that they can be used. Basically, if you have a superconducting wire and then a small gap of a different material that's not superconducting, an insulator or normal metal, and then another superconducting wire on the other side, that's a Josephson junction. So it's sometimes referred to as a superconducting weak link. So you have this.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“This material, you can do usual things like make wires out of it so you can get current to flow in a straight line on a chip, but you can also make other devices that perform different kinds of operations. Some of them are kind of logic operations like you'd get in a transistor. the most common or the most I would say diverse in its utility component is a Joseph's injunction. It's not analogous to a transistor in the sense that if you apply a voltage here it changes how much current flows from left to right but it is analogous in sort of a sense of it's the it's the go-to component that a that a circuit engineer is going to use to start to build up more complexity”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, we let's try to be a little bit light on the technical details, but essentially the electrons coordinate with each other. They are able to, in this macroscopic quantum state, they're able to sort of, one can quickly take the place of the other. You can't tell electrons apart. They're what's known as identical particles. Can just sort of almost miraculously avoid that defect because it's not really in that location. It's part of a macroscopic quantum state and the entire quantum state was not scattered by that defect. So you can get a current that flows without dissipation. And that's called a supercurrent. That's sort of just very much scratching the surface of superconductivity. There's very deep and rich physics there, which is probably not the main subject we need to go into right now, but it turns out that when you have”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Atoms moving around, the lattice vibrating, electrons colliding with each other that becomes sufficiently low that the electrons can settle into this very special state. It's sometimes referred to as a macroscopic quantum state because if I had a piece of superconducting material here, let's say niobium is a very typical superconductor. If I had a block of niobium here and we cooled it below its critical temperature, all of the electrons in that superconducting state would be in one coherent quantum state. The wave function of that state is described in terms of all of the particles simultaneously, but it extends across macroscopic dimensions, the size of whatever material, the size of whatever block of that material I have sitting here. And the way this occurs is that”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Happens at low temperature, and this is crucial. It has to be a quite low temperature. And what I'm talking about there, for essentially all of our conversation, I'm going to be talking about conventional superconductors, sometimes called low TC superconductors, low critical temperature superconductors. And so those materials have to be at a temperature around, say, around 4 Kelvin. I mean, their critical temperature might be 10 Kelvin, something like that, but you want to operate them at around 4 Kelvin, four degrees above absolute zero. And what happens at that temperature at that very low temperatures in certain materials is that the noise of”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, okay. So in a semiconductor, as I try to describe a second ago, you can sort of Currents by applying voltages, and those have sort of typical properties that you would expect from some kind of a conductor. Those electrons, they don't just flow perfectly without dissipation. If an electron collides with an imperfection in the lattice or another electron, it's going to slow down. It's going to lose its momentum. So you have to keep applying that voltage in order to keep the current flowing. And a superconductor is something different happens. If you get a current to start flowing, it will continue to flow indefinitely. There's no dissipation. So that's crazy. How does that happen? Well,”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“No, I would say that it is basic physics, it is applied physics, it's material science, it's x-ray crystallography, it's polymer chemistry, it's everything.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Why we have computers more powerful than old supercomputers in each of our phones, that's all engineering. And I think I would be quite foolish to say that that's not valuable, that that's not a great contribution.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Gets more credit. I'm genuinely not trying to just be politically correct here. I don't see how you would have. Any of what we consider sort of the great accomplishments of society without both. You absolutely need both of those things. Physics tends to play a key role earlier in the development. Engineering optimization, these things take over. And I mean, the invention of the transistor, or actually even before that, the understanding of semiconductor physics that allowed the invention of the transistor, that's all physics. So if you didn't have that physics, you don't even get to get on the field. But once you have understood and demonstrated that this is in principle possible, Moore's Law is engineering.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Accidental carriers that are excited into the conduction band and it causes errors in your computation. Silicon's band gap is just a little higher, 1.1 electron volts, but you have an exponential dependence on the number of carriers that are present that can induce those errors. It decays exponentially with that voltage. So just that slight extra energy in that band gap really puts it in an ideal position to be operated in the conditions of our ambient environment.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Is actually so in a semiconductor, there's an important parameter which is called the band gap, which tells you they're sort of electrons that fill up to one level in the energy diagram. And then there's a gap where electrons aren't allowed to have an energy in a certain range. And then there's another energy level above that. And that difference between the lower sort of filled level and the unoccupied level, that tells you how much voltage you have to apply in order to induce a current to flow. So with germanium, that's about 0.75 electron volts. That means you have to apply 0.75 volts to get a current moving. And it turns out that if you compare that to the thermal excitations that are induced just by the temperature of our environment, that gap's not quite big enough. You start to use it to perform computations. It gets a little hot and you get all these.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Why did Silicon win? It's because of a remarkable assemblage of qualities that no one of them was the clear winner, but it made these sort of compromises between a number of different influences. It had that really excellent gate oxide that allowed us to make MOSFETs, these high performance transistors so quickly and cheaply and easily without having”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Properties that might be conducive to making the best devices. So there were different groups exploring each of these, and that's great. That's how science works. You have to cast a broad net. But then what I find striking is why is it that silicon won? Because it's not that germanium is a useless material and it's not present in technology or compound semiconductors. They're both doing exciting and important things slightly more niche applications where a silicon is the semiconductor material for microelectronics, which is the platform for digital computing, which has transformed our world.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“They had different properties, different strengths, different weaknesses. Most people thought germanium was the way to go. It had some nice properties related to things about how the electrons move inside the lattice. But other people thought that compound semiconductors with group three and group five also had really, really extraordinary.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“The most beautiful is a little difficult to answer. Let me try and sidestep it a little bit and just say what strikes me about looking at the history of silicon microelectronics is that so when quantum mechanics was developed, people quickly began applying it to semiconductors. And it was broadly understood that these are fascinating systems and people cared about them for their basic physics, but also their utility as devices. the transistor was invented in the late 40s in a relatively crude experimental setup where you just crammed a metal electrode into the semiconductor and and that was that was ingenious these people were able to make it work you know uh but so what what i want to get to that that really strikes me is that in those early days there there were a number of different semiconductors that were being”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“We have access to light sources that can produce these very short wavelengths of light. How does photolithography occur? Well, you actually put this polymer on top of your wafer and you expose it to light and then you use a aqueous chemical processing to dissolve away the regions that were exposed to light and leave the regions that were not. And we are blessed with these polymers that have the right property where they can cause scission events where the polymer splits where a photon hits. I mean, you know, maybe that's not too surprising, but I don't know. It all comes together to have this really complex manufacturable ecosystem where very sophisticated technologies can be devised and it works quite well.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Through it. So that's physics right there. There are other things too. Silicon is a semiconductor in an elemental sense. You only need silicon atoms. A lot of other semiconductors you need two different kinds of atoms like a compound from group three and a compound from group five. That opens you up to lots of defects that can occur where one atom's not sitting quite at the lattice site it is and it's switched with another one that degrades performance. But then also on the side that you mentioned with the manufacturing.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“That was silicon dioxide, which just naturally grows on the silicon surface. So you expose silicon to the atmosphere that we breathe and, well, if you're manufacturing, you're going to purify these gases. But nevertheless, that what's called a native oxide will grow there. There are essentially no other materials on the entire periodic table that have as good of a gate insulator as that silicon dioxide. And that has to do with nothing but the physics of the interaction between silicon and oxygen. And if it wasn't that way, transistors could not perform in nearly the degree of capability that they have. And that has to do with the way that the oxide grows, the reduced density of defects there. It's insulation, meaning essentially its energy gaps. You can apply a very large voltage there without having current leaks.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“All of the things you just said. So starting with the silicon material itself, silicon is a unique semiconductor. It has essentially ideal properties for making a specific kind of transistor that's extraordinarily useful. So I mentioned that silicon has this, well, when you make a transistor, you have this gate contact that sits on top of the conduction channel. And depending on the voltage you apply there, you pull more carriers into the conduction channel or push them away so it becomes more or less conductive in order to have that work without just sucking those carriers right into that contact. You need a very thin insulator. And part of scaling has been to gradually decrease the thickness of that gate insulator so that you can use a roughly similar voltage and still have the same current voltage characteristics. So the material that's used to do that, or I should say, was initially used to do.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Manufacturing scalability, which I will just emphasize, I believe is enabled by physics. It's not, I mean, of course, there's human ingenuity that goes into it, but at least from my side where I sit, it sure looks like the physics of our universe allows us to produce that. And we've discovered how more so than we've invented it, although, of course, we have invented it. Humans have invented it, but it was almost as if it was there. waiting for us to discover it.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“Using photo lithography, you basically print the same pattern on different dies all across the wafer multiple layers tends probably 100 some layers in a mature foundry process and you do this on ever bigger wafers too. That's another aspect of scaling that's occurred in the last several decades. So now you have this 300 millimeter wafer. It's like as big as a pizza and it has maybe a thousand processors on it. And then you dice that up using a saw. And now you can sell these things so cheap because the manufacturing process was so streamlined. I think a technology is revolutionary as”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source
“And you say you're at seven nanometers. Well, the wavelength of light that's being used is over 100 nanometers. That's already deep in the UV. So how are those minute features patterned? Well, there's an extraordinary amount of innovation that has gone into that. But nevertheless, it stayed very consistent in this ever-shrinking feature size. And now the question is, can you make it smaller? And even if you do, do you still continue to get performance improvements? But that's another kind of scaling where these companies have been able to, so okay, you picture a chip that has a processor on it. Well, that chip is not made as a chip. It's made as an a wafer.”
2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source