Taking inspiration from biology and direction from some very weird math, a team of engineers have made electric wires that amplify signals traveling along them. Without the help of amplifiers or other devices, signals carried on wires as long as 1 millimeter came out stronger than they went in. The team hopes these devices, which are analogous to the axons that carry signals from our nerve cells, will enable future engineers to completely rethink how computer chips are designed. This work was published on 11 September in the journal Nature.
In electrical engineering, “we just take it for granted that the signal decays” as it travels, says Timothy Brown, a postdoc in materials physics at Sandia National Lab who was part of the group of researchers who made the self-amplifying device. Even the best wires and chip interconnects put up resistance to the flow of electrons, degrading signal quality over even relatively small distances. This constrains chip designs—lossy interconnects are broken up into ever smaller lengths, and signals are bolstered by buffers and drivers. A 1-square-centimeter chip has about 10,000 repeaters to drive signals, estimates R. Stanley Williams, a professor of computer engineering at Texas A&M University.
Williams is one of the pioneers of neuromorphic computing, which takes inspiration from the nervous system. Axons, the electrical cables that carry signals from the body of a nerve cell to synapses where they connect with projections from other cells, are made up of electrically resistant materials. Yet they can carry high fidelity signals over long distances. The longest axons in the human body are about 1 meter, running from the base of the spine to the feet. Blue whales are thought to have 30 m long axons stretching to the tips of their tails. If something bites the whale’s tail, it will react rapidly. Even from 30 meters away, “the pulses arrive perfectly,” says Williams. “That’s something that doesn’t exist in electrical engineering.”
Signals on chip interconnects often must be amplified multiple times to make travel long distances.
That’s because axons are active transmission lines: they provide gain to the signal along their length. Williams says he started pondering how to mimic this in an inorganic system 12 years ago. A grant from the US Department of Energy enabled him to build a team with the necessary resources to make it happen. The team included Williams, Brown, and Suhas Kumar, a materials physicist at Sandia.
Axons are coated with an insulating layer called the myelin sheath. Where there are gaps in the sheath, negatively charged sodium ions and positively charged potassium ions can move in and out of the axon, changing the voltage across the cell membrane and pumping in energy in the process. Some of that energy gets taken up by the electrical signal, amplifying it.
Neuromorphic Devices at the Edge of Chaos
Williams and his team wanted to mimic this in a simple structure. They didn’t try to mimic all the physical structures in axons—instead, they sought guidance in a mathematical description of how they amplify signals. Axons operate in a mode called the “edge of chaos,” which combines stable and unstable qualities. This may seem inherently contradictory. Brown likens this kind of system to a saddle that’s curved with two dips. The saddle curves up towards the front and the back, keeping you stable as you rock back and forth. But if you get jostled from side to side, you’re more likely to fall off. When you’re riding in the saddle, you’re operating at the edge of chaos, in a semistable state. In the abstract space of electrical engineering, that jostling is equivalent to wiggles in current and voltage.
The mathematics behind the edge of chaos were worked out by Leon Chua, professor emeritus of electrical engineering and computer science at the University of California, Berkeley. (Chua also developed the theory behind the memristor, a device that holds a memory of the current that has passed through it; Williams was the first to build one.) The math tells us that this self amplifying, edge-of-chaos behavior should exist in materials with the right properties. But Williams and his team had to find the right material, one that could do what the math says is possible.
The team chose lanthanum colbalt oxide—it seemed to have the right properties, and they were able to obtain some. The key material property they sought was nonlinearity. This material has a nonlinear change in its resistance as a function of temperature. (Imagine a current versus voltage curve shaped like an “S”.) As current flows into the material, its temperature rises, and its resistance changes. Under the right set of conditions—when seated just right in the electrical saddle—this material should apply negative resistance. That is, it should amplify a signal.
Signals traveling down neuronal axons exhibit a self-amplifying behavior because they exist at a so-called edge of chaos (EOC) state. Researchers found a way to mimic that in the behavior of lanthanum cobalt oxide.
The team demonstrated this in a device is made up of a layer of LaCoO3 with a 1 mm metal line on top of it. They biased the LaCoO3 with a direct current, and passed an alternating current signal through the metal.
Williams says he almost fell out of his chair when he saw Brown’s oscilloscope measurements. Not only did the signal not degrade as it passed through this device—it came out the other side of the wire amplified by as much as 70 percent. “We’ve shown that edge of chaos is a property of materials—it’s physically real,” Williams says.
There’s a long way to go from this first experimental demonstration to a reimagining of computer chip interconnects. The team is providing samples for other researchers who want to verify their measurements. And they’re trying other materials to see how well they do—LaCoO3 is only the first one they’ve tested.
Williams hopes this research will show electrical engineers new ideas about how to move forward. “The dream is to redesign chips,” he says. Electrical engineers have long known about nonlinear dynamics, but have hardly ever taken advantage of them, Williams says. “This requires thinking about things and doing measurements differently than they have been done for 50 years,” he says.
