Graphene transistors are really fast

Fast like already an order of magnitude faster than the quickest silicon transistors. The IBM prototype graphene transistors run at 100 gigahertz.

From the link:

The transistors were created using processes that are compatible with existing semiconductor manufacturing, and experts say they could be scaled up to produce transistors for high-performance imaging, radar, and communications devices within the next few years, and for zippy computer processors in a decade or so.

Researchers have previously made graphene transistors using laborious mechanical methods, for example by flaking off sheets of graphene from graphite; the fastest transistors made this way have reached speeds of up to 26 gigahertz. Transistors made using similar methods have not equaled these speeds.

Growing transistors on a wafer not only leads to better performance, it’s also more commercially feasible, says Phaedon Avouris, leader of the nanoscale science and technology group at the IBM Watson Research Center in Ossining, NY where the work was carried out.

Speedy switches: These arrays of transistors, printed on a silicon carbide wafer, operate at speeds of 100 gigahertz.

Credit: Science/AAAS

Friday video fun — news online circa 1981

A report from some of the earliest days of consumer computer connectivity …

(Hat tip: smartsavvy)

Ten unusual computers

Here’s a NewScientistTech story on ten weird computers. Now the top few aren’t really all that strange — they include optical, quantum and DNA. After that things do get a bit esoteric.

Then there’s the bottom of the list.

Numbers eight, nine and ten are where things really get weird …

8. Glooper Computer

One of the weirdest computers ever built forsakes traditional hardware in favour of “gloopware”. Andrew Adamatzky at the University of the West of England, UK, can make interfering waves of propagating ions in a chemical goo behave like logic gates, the building blocks of computers.

The waves are produced by a pulsing cyclic chemical reaction called the Belousov-Zhabotinsky reaction.

Adamatzky has shown that his chemical logic gates can be used to make a robotic hand stir the mixture in which they exist. As the robot’s fingers stimulate the chemicals further reactions are triggered that control the hand.

The result is a sort of robotic existential paradox – did the chemical brain make the robot’s hand move, or the hand tell the brain what to think? Eventually Adamatzky aims to couple these chemical computers to an electroactive gel-based “skin” to create a complete “blob-bot”.

9. Mouldy computers

Even a primitive organism like slime mould can be used to solve problems that are tricky for classical computers.

Toshiyuki Nakagaki at the Institute of Physical and Chemical Research in Nagoya, Japan, has shown that slime mould can work out the shortest route through a maze.

In his experiments, the masses of independent amoeba-like cells that act as a single organism would initially spread out to explore all the possible paths of a maze.

But when one train of cells found the shortest path to some food hidden at the maze’s exit the rest of the mass stopped exploring. The slime mould then withdrew from the dead end routes and followed the direct path to the food.

This is interesting for computer scientists because maze solving is similar to the travelling salesman problem, which asks for the shortest route between a number of points in space. The problem quickly scales in complexity as more points are added, making it a tough problem for classical computers.

10. Water wave computing

Perhaps the most unlikely place to see computing power is in the ripples in a tank of water.

Using a ripple tank and an overhead camera, Chrisantha Fernando and Sampsa Sojakka at the University of Sussex, used wave patterns to make a type of logic gate called an “exclusive OR gate”, or XOR gate.

Perceptrons, a type of artificial neural network, can mimic some types of logic gates, but not a XOR. Only encoding the behaviour of a XOR gate into ripples made it possible for the perceptron to learn how that gate works.

(Hat tip: KurzweilAI.net)