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Researchers at Stanford University have built one of the most complex circuits from carbon nanotubes yet. They showed off a simple hand-shaking robot with a sensor-interface circuit last week at the International Solid-State Circuits Conference in San Francisco.

As the silicon transistors inside today’s computers reach their physical limits, the semiconductor industry is eyeing alternatives, and one of the most promising is carbon nanotubes. Tiny transistors made from these nanomaterials are faster and more energy efficient than silicon ones, and computer models predict that carbon nanotube processors could be an order of magnitude less power hungry. But it’s proved difficult to turn individual transistors into complex working circuits (see “How to Build a Nano-Computer”).

The demonstration carbon nanotube circuit converts an analog signal from a capacitor—the same type of sensor found in many touch screens—into a digital signal that’s comprehensible by a microprocessor. The Stanford researchers rigged a wooden mannequin hand with the capacitive switch in its palm. When someone graspsed the hand, turning on the switch, the nanotube circuit sent its signal to the computer, which activated a motor on the robot hand, moving it up and down to shake the person’s hand.

Other researchers have demonstrated simple nanotube circuits before, but this is the most complex made so far, and it also demonstrates that nanotube transistors can be made at high yields, says Subhasish Mitra, an associate professor of electrical engineering and computer science, who led the work with Philip Wong, a professor of electrical engineering at Stanford.

The nanotube circuit is still relatively slow—its transistors are large and far apart compared to the latest silicon circuits. But the work is an important experimental demonstration of the potential of carbon nanotube computing technology.

“This shows that carbon nanotube transistors can be integrated into logic circuits that perform at low voltage,” says Aaron Franklin, who is developing nanotube electronics at the IBM Watson Research Center. This feat has been demonstrated by Franklin’s group at the single-transistor level, and been shown to be theoretically possible by others, but seeing it in a complex circuit is important, says Franklin.

Working with carbon nanotubes presents many challenges—as many as 30 percent of them are metallic, rather than semiconducting, with the potential to burn out a circuit. Nanotubes also tend to grow in a spaghetti-like tangle, which can cause circuits to switch unpredictably. The approach taken by the Stanford group is to work with their imperfections, coming up with error-tolerant circuit design techniques that allow them to build circuits that work even when the starting materials are flawed. “We want to build up the circuit complexity, then go back to improving the building methods, then make more complex circuits,” says Wong.

“This is no different from the early days in silicon,” says Ashraf Alam, professor of electrical and computer engineering at Purdue University. Compared to the electronics in today’s silicon-based smartphones and supercomputers, the first silicon transistors were poor quality, as were the first integrated circuits. But silicon got through its growing pains, and the semiconductor industry perfected building ever-denser arrays of integrated circuits made up of ever-smaller transistors.

“Variation and imperfection are going to be the air we breathe in semiconductor technology,” says Wong, not just for those working with new materials, but for conventional silicon technology, too. Today’s state-of-the-art chips use 22-nanometer transistors—billions on each chip—and there is very little variation in their performance; the semiconductor industry has mastered making these tiny devices at tremendous scales, and with very high yields.

The drive to continually miniaturize transistors while maintaining scrupulous quality control has enabled technologies ranging from smartphones and supercomputers. But unavoidable flaws, at the level of single atoms, will soon lead to variation in performance that will have to be accounted for in circuit design. “Error-tolerant design has to be part of the way forward, because we will never get the materials completely perfect,” says Wong.

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Credit: Robust Systems Group, Stanford University

Tagged: Computing

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