Breaking the Silicon Barrier: Graphene Transistors Demonstrated
March 2, 2007 1:47 AM
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Professor Andre Geim of The School of Physics and Astronomy at The University of Manchester
Dr. Kostya Novoselov of The School of Physics and Astronomy at The University of Manchester
Graphene-based transistor created by the University of Manchester team
The largest hurdle in semiconductor miniaturization has just been shattered
Using the world’s thinnest material, Graphene, researchers at the University of Manchester have
created the world’s smallest transistor
. According to Professor Andre Geim and Dr. Kostya Novoselov from The School of Physics and Astronomy at The University of Manchester, the new transistors are only one atom thick and less than 50 atoms wide. The development opens the gate to superfast computer chips at sizes not possible before with standard Silicon transistors.
According to the semiconductor industry roadmap, miniaturization of electronics will face its largest challenge in the next twenty years. This is because Silicon based technology will begin to reach its minimum size limit.
Graphene, a form of carbon that is only one atom thick, may provide a solid alternative for even further miniaturization of electronics as silicon-based technology reaches its limit.
Graphene transistors were originally created two years ago, but at that time they were very “leaky” meaning current could not be turned off to zero. The “leaky” quality of the transistors effectively limited their uses, and rendered them useless for employment in computer chips and electronic circuits. But over the course of the past two years the research team at the University of Manchester was able to overcome this problem, and have created fully-functional and stable Graphene transistors.
Graphene transistors remain stable and conductive even when they are only a few nanometers wide. This is in contrast to all other known materials, including the dominant silicon transistors, which “oxidize, decompose and become unstable at sizes ten times larger.” This is the barrier that current silicon-based technology is approaching and is likely to also be its downfall.
"We have made ribbons only a few nanometers wide and cannot rule out the possibility of confining graphene even further - down to maybe a single ring of carbon atoms," says Professor Geim of the University of Manchester.
Graphene provides a solid alternative to Silicon and according to Geim can lead to even further reductions in size. Geim expects future electronic circuits to possibly be carved out of a single Graphenesheet.
Dr Leonid Ponomarenko, who is leading this research at The University of Manchester, is optimistic of the technologies’ future.
"The next logical step is true nanometer-sized circuits and this is where graphene can come into play because it remains stable - unlike silicon or other materials - even at these dimensions."
Geim believes that Graphene is the only viable successor to Silicon after the currently dominant technology reaches its limit. Graphene-based circuits, however, are not likely to be completely ready until 2025.
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RE: What I'm wondering is
3/2/2007 5:20:51 PM
Quantum computers can be used to perform standard operations as well, provided implementation.
Quantum mechanics is the ideal route for computing to take.
RE: What I'm wondering is
3/2/2007 5:46:40 PM
As I said, a quantum computer can only run stochastic (randomized) algorithms. To perform a 'standard' operation, you need a randomized version of that operation. So far, we've found very few of those (e.g. Shor's algorithm for factoring, etc). It's not even clear whether stochastic algorithms exist for all problems and, even if they do, whether or not they'll be efficient.
It's already been proven that, for an
NP-complete problem in which an efficient stochastic algortithm doesn't exist, a quantum computer provides only a quadratic speedup, which means they're really not any more useful than a standard computing device.
RE: What I'm wondering is
3/2/2007 7:01:31 PM
Thats why 'current' quantum computers are complimented with a basic cpu to randomize data streams. The performance can be impressive when you consider it takes the same amount of time to add 1 + 1 than it does to find a quadratic such as x = ( -b +- squareroot( (b) squared - 4ac ) ) / (2a)
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