Karl Skjonnemand – TED Talk TRANSCRIPT
Computers used to be as big as a room. But now they fit in your pocket, on your wrist and can even be implanted inside of your body.
How cool is that? And this has been enabled by the miniaturization of transistors, which are the tiny switches in the circuits at the heart of our computers. And it’s been achieved through decades of development and breakthroughs in science and engineering and of billions of dollars of investment.
But it’s given us vast amounts of computing, huge amounts of memory and the digital revolution that we all experience and enjoy today.
But the bad news is, we’re about to hit a digital roadblock, as the rate of miniaturization of transistors is slowing down. And this is happening at exactly the same time as our innovation in software is continuing relentlessly with artificial intelligence and big data.
And our devices regularly perform facial recognition or augment our reality or even drive cars down our treacherous, chaotic roads. It’s amazing. But if we don’t keep up with the appetite of our software, we could reach a point in the development of our technology where the things that we could do with software could, in fact, be limited by our hardware.
We’ve all experienced the frustration of an old smartphone or tablet grinding slowly to a halt over time under the ever-increasing weight of software updates and new features. And it worked just fine when we bought it not so long ago.
But the hungry software engineers have eaten up all the hardware capacity over time. The semiconductor industry is very well aware of this and is working on all sorts of creative solutions, such as going beyond transistors to quantum computing or even working with transistors in alternative architectures such as neural networks to make more robust and efficient circuits.
SLOWDOWN OF MINIATURIZATION
But these approaches will take quite some time, and we’re really looking for a much more immediate solution to this problem. The reason why the rate of miniaturization of transistors is slowing down is due to the ever-increasing complexity of the manufacturing process. The transistor used to be a big, bulky device, until the invent of the integrated circuit based on pure crystalline silicon wafers.
And after 50 years of continuous development, we can now achieve transistor features dimensions down to 10 nanometers. You can fit more than a billion transistors in a single square millimeter of silicon.
And to put this into perspective: a human hair is 100 microns across. A red blood cell, which is essentially invisible, is eight microns across, and you can place 12 across the width of a human hair. But a transistor, in comparison, is much smaller, at a tiny fraction of a micron across.
You could place more than 260 transistors across a single red blood cell or more than 3,000 across the width of a human hair. It really is incredible nanotechnology in your pocket right now.
And besides the obvious benefit of being able to place more, smaller transistors on a chip, smaller transistors are faster switches, and smaller transistors are also more efficient switches. So this combination has given us lower cost, higher performance and higher efficiency electronics that we all enjoy today.
To manufacture these integrated circuits, the transistors are built up layer by layer, on a pure crystalline silicon wafer. And in an oversimplified sense, every tiny feature of the circuit is projected onto the surface of the silicon wafer and recorded in a light-sensitive material and then etched through the light-sensitive material to leave the pattern in the underlying layers.
And this process has been dramatically improved over the years to give the electronics performance we have today.
But as the transistor features get smaller and smaller, we’re really approaching the physical limitations of this manufacturing technique. The latest systems for doing this patterning have become so complex that they reportedly cost more than 100 million dollars each. And semiconductor factories contain dozens of these machines.
So people are seriously questioning: Is this approach long-term viable? But we believe we can do this chip manufacturing in a totally different and much more cost-effective way using molecular engineering and mimicking nature down at the nanoscale dimensions of our transistors.
As I said, the conventional manufacturing takes every tiny feature of the circuit and projects it onto the silicon. But if you look at the structure of an integrated circuit, the transistor arrays, many of the features are repeated millions of times. It’s a highly periodic structure. So we want to take advantage of this periodicity in our alternative manufacturing technique.
We want to use self-assembling materials to naturally form the periodic structures that we need for our transistors. We do this with the materials, then the materials do the hard work of the fine patterning, rather than pushing the projection technology to its limits and beyond.
Self-assembly is seen in nature in many different places, from lipid membranes to cell structures, so we do know it can be a robust solution. If it’s good enough for nature, it should be good enough for us. So we want to take this naturally occurring, robust self-assembly and use it for the manufacturing of our semiconductor technology.