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Jeffrey Shainline

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208
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2021-09-26
most recent
2021-09-26
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1
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podcast

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  1. “Right, right. So that's another thing. So, how have we been able to make those transistors smaller and smaller? Well, companies like Intel, Global Foundries, they invest a lot of money in the lithography. So how are these chips actually made? Well, one of the most important steps is this, what's called ion implantation. So you start with sort of a pristine silicon crystal, and then using photolithography, which is a technique where you can pattern different shapes using light, you can define which regions of space you're going to implant with different species of ions that are going to change the local electrical properties right there. So by using ever shorter wavelengths of light and different kinds of optical techniques and different kinds of lithographic techniques, things that go far beyond my knowledge base, you can just simply shrink that feature size.”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “Transistor has just a few tens of atoms along the length of the conduction pathway. So a naive semiconductor device physicist would think you can't go much further than that without some kind of revolution in the way we think about the physics of our devices.”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “Just continues to improve. And we don't have to think too hard about what we're doing as, say, a software designer or something like that. I absolutely don't mean to say that there's no innovation in software or the user side of things. Of course, there is, but from the hardware perspective, we just have been given this gift of continued performance improvement through this scaling that is ever smaller feature sizes with very similar, say, power consumption. That power consumption has not continued to scale in the most recent decades, but nevertheless we had a really good run there for a while. And now we're down to gates that are seven nanometers, which is state-of-the-art right now, maybe global foundries is trying to push it even lower than that. I can't keep up with where the predictions are that it's going to end. But seven nanometer”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “What has enabled what we think of as Moore's law or the continued increased performance in silicon microelectronic circuits is the ability to make that size, that feature size ever smaller, ever smaller at a really remarkable pace. I mean, that feature size has decreased consistently every couple of years for since the 1960s. And that was what Moore predicted in the 1960s. He thought it would continue for at least two more decades, and it's been much longer than that. And so that is why we've been able to fit ever more devices, ever more transistors, ever more computational power on essentially the same size of chip. So a user sits back and does essentially nothing. You're running the same computer program, but those devices are getting smaller, so they get faster, they get more energy efficient, and all of our computing performance.”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Yeah, so scale in a number of different senses. Well, at the scale of the silicon lattice, the distance between two atoms there is half a nanometer. So people often like to compare these things to the width of a human hair. I think it's some six orders of magnitude smaller than the width of a human hair. Something on that order. So remarkably small. We're talking about individual atoms here, and electrons are of that length scale when they're in that environment. But there's another sense that scale matters in digital electronics. This is perhaps the more important sense, although they're related. Scale refers to a number of things. It refers to the size of that transistor. So, for example, I said you have a left contact, a right contact, and some space between them where the gate electrode sits. That's called the channel width or the channel length.”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “From that basic element, you can build up all the complexity of digital electronic circuits that have really had a profound influence on our society.”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “Cause electrical current to flow between them. Now we add a third terminal up on top there, and depending on the voltage between the left and right terminal and that third voltage, you can change that current. So what's commonly done in digital electronic circuits is to leave a fixed voltage from left to right and then change that voltage that's applied at what's called the gate, the gate of the transistor. What you do is you make it to where there's an excess of electrons on the left, excess of electrons on the right, and very few electrons in the middle. And you do this by changing the concentration of different dopants in the lattice spatially. And then when you apply a voltage to that gate, you can either cause current to flow or turn it off. And so that's sort of your zero and one. If you apply voltage, current can flow. That current is representing a digital one. And from that,”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “You can change the number of free electrons that can move around by putting different elements, different atoms in lattice sites. So what is a lattice site? Well, a semiconductor is a crystal, which means all the atoms that comprise the material are at exact locations that are perfectly periodic in space. So if you started any one atom and you go along the what are called the lattice vectors, you get to another atom and another atom and another atom. And for high quality devices, it's important that it's a perfect crystal with very few defects. But you can intentionally replace a silicon atom with, say, a phosphorus atom. And then you can change the number of free electrons that are in a region of space that has that excess of what are called dopants. So picture a device that has a left terminal and a right terminal. If you apply a voltage between those two, you can”

    2021-09-26 · Lex Fridman Podcast · #225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence · IDENTIFIED FROM THE TRANSCRIPT · source