IBM seriously just turned an atom into the world’s smallest hard drive.


Data storage technology continues to shrink in size and grow in capacity, but scientists have just taken things to the next level – they’ve built a nanoscale hard drive using a single atom.

By magnetising an atom, cooling it with liquid helium, and storing it in an extreme vacuum, the team managed to store a single bit of data (either a 1 or a 0) in this incredibly miniscule space.

 

Not enough room for your holiday photos then, but according to the team from IBM Research in California, this proof-of-concept approach could eventually lead to drives the size of a credit card that could hold the entire iTunes or Spotify libraries, at about 30 million songs each.

“We conducted this research to understand what happens when you shrink technology down to the most fundamental extreme – the atomic scale,” says one of the researchers, nanoscientist Christopher Lutz.

The team deployed its Nobel Prize-winning Scanning Tunneling Microscope (STM) for the experiment, which uses the ‘tunnelling phenomenon‘ in quantum mechanics, where electrons can be pushed through barriers, to study electronics at the atomic scale.

With the extreme vacuum conditions inside the STM, free from air molecules and other types of contamination, scientists were able to successfully manipulate a holmium atom.

The microscope also applies liquid helium cooling, which is important in adding stability to the magnetic reading and writing process.

Thanks to that carefully controlled environment, the team could accurately read and write two magnetically charged atoms just a single nanometre apart – that’s one millionth the width of a pinhead.

With the help of the microscope, the scientists could deliver an electric current that turns the magnetic orientation of a single atom up or down, mimicking the operation of a normal hard drive, but on a much smaller scale.

Today’s hard drives use about 100,000 atoms to store a single bit, so you can get an idea of the difference we’re talking about.

The team says the technique could produce drives that are 1,000 times denser than the ones we have right now.

And while the process is going to remain much too difficult and expensive to use commercially for some time, the researchers have shown that it can be done, which is an exciting first step.

 This is just the latest in a long line of innovations in data storage – earlier this month researchers from Columbia University announced they’d crammed six digital files into a single speck of DNA.

While there have been previous efforts to store data on single atoms, this is now the smallest and most stable result yet, according to the IBM team.

“The high magnetic stability combined with electrical reading and writing shows that single-atom magnetic memory is indeed possible,” the researchers conclude.

Watch the video. URL:https://youtu.be/2laKpYWIa5I

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Quantum Computers Could Crush Today’s Top Encryption in 15 Years


Quantum computers could bring about a quantum leap in processing power, with countless benefits for fields like data science and AI. But there’s also a dark side: this extra power will make it simple to crack the encryption keeping everything from our emails to our online banking secure.

A recent report from the Global Risk Institute predicted that there is a one in seven chance vital cryptography tools will be rendered useless by 2026, rising to a 50% chance by 2031. In the meantime, hackers and spies can hoover up data encrypted using current approaches and simply wait until quantum computers powerful enough to crack the code have been developed.

quantum-computers-encryption-7The threat to encryption from quantum computers stems from the fact that some of the most prevalent approaches rely on solving fiendishly complicated mathematical problems. Unfortunately, this is something quantum computers are expected to be incredibly good at.

While traditional computers use binary systems with bits that can either be represented as 0 or 1, a quantum bit—or “qubit”—can be simultaneously 0 and 1 thanks to a phenomenon known as superposition. As you add qubits to the systems this means the power of the computer grows exponentially, making quantum computers far more efficient.

In 1994 Peter Shor of Bell Laboratories created a quantum algorithm that can solve a problem called integer factorization. As a report from the National Institute of Standards and Technology (NIST) released in April notes, this algorithm can be used to efficiently solve the mathematical problems at the heart of three of the most widely-used encryption approaches: Diffie-Hellman key exchange, RSA, and elliptic curve cryptography.

The threat is not imminent, though; building quantum computers is difficult. Most designs rely on complex and expensive technology like superconductors, lasers and cryogenics and have yet to make it out of the lab. Google, IBM and Microsoft are all working on commercializing the technology. Canadian company D-Wave is already selling quantum computers, but capabilities are still limited.

The very laws of quantum mechanics that makes these computers so powerful also provide a way to circumvent the danger. Quantum cryptography uses qubits in the form of photons to transmit information securely by encoding it into the particles’ quantum states. Attempting to measure any property of a quantum state will alter another property, which means attempts to intercept and read the message can be easily detected by the recipient.

quantum-computers-encryption-2The most promising application of this approach is called quantum key distribution, which uses quantum communication to securely share keys that can be used to decrypt messages sent over conventional networks. City-wide networks have already been demonstrated in the US, Europe and Japan, and China’s newest satellite is quantum communication-enabled.

But the systems are held back by low bandwidth and the fact they only work over short distances. China is trying to build a 2,000km-long quantum network between Shanghai and Beijing, but this will require 32 “trusted nodes” to decode the key and retransmit it, introducing complexity and potential weaknesses to the system.

There’s also no guarantee quantum communication will be widely adopted by the time encryption-cracking quantum computers become viable. And importantly, building a single powerful encryption-busting quantum computer would require considerably less resources than restructuring entire communication networks to accommodate quantum cryptography.

Fortunately, there are other approaches to the problem that do not rely on quantum physics. So-called symmetric-key algorithms are likely to be resistant to quantum attacks if the key lengths are doubled, and new approaches like lattice-based, code-based and multi-variate cryptography all look likely to be uncrackable by quantum computers.

Symmetric-keys only work in a limited number of applications, though, and the other methods are still at the research stage. On the back of its report the NIST announced that it would launch a public competition to help drive development of these new approaches. It also recommends organizations focus on “crypto agility” so they can easily swap out their encryption systems as quantum-hardened ones become available.

But the document also highlighted the fact that it has taken roughly 20 years to deploy our current cryptography infrastructure. Just a month before the release of the report, researchers from MIT and the University of Innsbruck in Austria demonstrated a five-atom quantum computer capable of running Shor’s algorithm to factor the number 15.

Crucially, their approach is readily scalable, which the team says means building a more powerful quantum computer is now an engineering challenge rather than a conceptual one. Needless to say, the race is on.

IBM Creates A Molecule That Could Destroy All Viruses


One macromolecule to rule them all, from Ebola to Zika and the flu

flu virus

The influenza virus.

CDC/ Dr. Erskine. L. Palmer; Dr. M. L. Martin via Flickr

Finding a cure for viruses like Ebola, Zika, or even the flu is a challenging task. Viruses are vastly different from one another, and even the same strain of a virus can mutate and change–that’s why doctors give out a different flu vaccine each year. But a group of researchers at IBM and the Institute of Bioengineering and Nanotechnology in Singapore sought to understand what makes all viruses alike. Using that knowledge, they’ve come up with a macromolecule that may have the potential to treat multiple types of viruses and prevent them from infecting us. The work was published recently in the journal Macromolecules.

For their study, the researchers ignored the viruses’ RNA and DNA, which could be key areas to target, but because they change from virus to virus and also mutate, it’s very difficult to target them successfully.

Instead, the researchers focused on glycoproteins, which sit on the outside of all viruses and attach to cells in the body, allowing the viruses to do their dirty work by infecting cells and making us sick. Using that knowledge, the researchers created a macromolecule, which is basically one giant molecule made of smaller subunits. This macromolecule has key factors that are crucial in fighting viruses. First, it’s able to attract viruses towards itself using electrostatic charges. Once the virus is close, the macromolecule attaches to the virus and makes the virus unable to attach to healthy cells. Then it neutralizes the virus’ acidity levels, which makes it less able to replicate.

As an alternative way to fight, the macromolecule also contains a sugar called mannose. This sugar attaches to healthy immune cells and forces them closer to the virus so that the viral infection can be eradicated more easily.

The researchers tested out this treatment in the lab on a few viruses, including Ebola and dengue, and they found that the molecule did work as they thought it would: According to the paper, the molecules bound to the glycoproteins on the viruses’ surfaces and reduced the number of viruses. Further, the mannose successfully prevented the virus from infecting immune cells.

This all sounds promising, but the treatment still has a ways to go before it could be used as a disinfectant or even as a potential pill that we could take to prevent and treat viral infections. But it does represent a step in the right direction for treating viruses: figuring out what is similar about all viruses to create a broad spectrum antiviral treatment.

IBM is one step closer to mimicking the human brain


A breakthrough in cognitive computing has enabled scientists to imitate large populations of neurons.

Scientists at IBM have claimed a computational breakthrough after imitating large populations of neurons for the first time.

Neurons are electrically excitable cells that process and transmit information in our brains through electrical and chemical signals. These signals are passed over synapses, specialised connections with other cells.

It’s this set-up that inspired scientists at IBM to try and mirror the way the biological brain functions using phase-change materials for memory applications.

Using computers to try to mimic the human brain is something that’s been theorised for decades due to the challenges of recreating the density and power. Now, for the first time, scientists have created their own “randomly spiking” artificial neurons that can store and process data.

“The breakthrough marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing,” the scientists said.

The artificial neurons consist of phase-change materials, including germanium antimony telluride, which exhibit two stable states, an amorphous one (without a clearly defined structure) and a crystalline one (with structure). These materials are also the basis of re-writable Blue-ray but in this system the artificial neurons do not store digital information; they are analogue, just like the synapses and neurons in a biological brain.

The beauty of these powerful phase-change-based artificial neurons, which can perform various computational primitives such as data-correlation detection and unsupervised learning at high speeds, is that they use very little energy – just like human brain.

In a demonstration published in the journal Nature Nanotechnology, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallisation of the phase-change material, ultimately causing the neuron to fire.

In neuroscience, this function is known as the integrate-and-fire property of biological neurons. This is the foundation for event-based computation and, in principle, is quite similar to how a biological brain triggers a response when an animal touches something hot, for instance.

“Populations of stochastic phase-change neurons, combined with other nanoscale computational elements such as artificial synapses, could be a key enabler for the creation of a new generation of extremely dense neuromorphic computing systems,” said Tomas Tuma, co-author of the paper.

This could be useful in sensors collecting and analysing volumes of weather data, for instance, said Sebastian, collected at the edge, in remote locations, for faster and more accurate weather forecasts.

The artificial neurons could also detect patterns in financial transactions to find discrepancies or use data from social media to discover new cultural trends in real time. While large populations of these high-speed, low-energy nano-scale neurons could also be used in neuromorphic co-processors with co-located memory and processing units.

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IBM just beat Google to a brand new type of computing


IBM Jerry Chow quantum computer scientist
IBM quantum computer scientist Jerry Chow.

On Wednesday, IBM scientists will make a quantum computer available to the public as a cloud service for the first time.Although the cloud service is geared mostly toward scientists and students, anyone interested in this strange new computer will be able to give it a try, Jerry Chow, one of the scientists leading the project, tells Business Insider.

A completely different kind of computer

A quantum computer is different than today’s digital computer.

A digital computer thinks in two states: zero and one (or off and on). A quantum computer uses “combinations of zeroes and ones” to creates multiple states. It can be a zero, a one, both at the same time, something in between them, or it can be a mysterious zero/one state that you can’t really determine, Chow explains.

These messy states are called “entanglement” and there are some well known algorithms (mathematical formulas) that use them, Chow tells us.

Because quantum computers think differently, they can quickly solve tasks that regular computers can’t do, such as working with billions of variables at the same time, like the interaction between molecules in chemistry.

They are also great for machine-learning tasks. These computers are expected to help find new drugs, new forms of computer security, and become smart computers that can think and reason.

Likewise, programming a quantum computer is completely different.

So the IBM team has created a tutorial to help people learn how to do it. You need high-school algebra skills and a background in programming. (It also helps to read a book on the subject before trying your first “Hello world” app, Chow advises.)

As cold as outer space

Quantum computers are also built differently. This one uses a silicon base, like regular computers, but relies on superconducting metals like niobium and aluminum that must be kept unbelievably cold. The low temperature brings out their special quantum mechanical properties.

IBM programmer lab

IBM

This is the microwave hardware that generates pulses sent to the quantum processor.

So it’s kept in a special fridge that keeps the computer at “.015 above absolute zero, which is colder than absolute space,” Chow says. (See picture, below.)

The computer behind this cloud service is a five “quantum bits” (qubits) computer, which is powerful (other quantum computers have been 2 qubits), but not so much smarter than a regular supercomputer.

However, the industry is working its way up to a 50 qubits computer which would be so vastly more powerful than any of today’s supercomputers.

No one knows what kinds of problems a computer that fast and smart could solve.

But there’s a race between IBM and Google to find out.

The race with Google is on

IBM’s work is based on research done at Yale through Professor Robert Schoelkopf (the IBM team is mostly his PhD and post-grad students).

The other prominent US school working on this is UC Santa Barbara under Professor John Martinis Group, which was backed and absorbed by Google in 2014.

“Google is working toward very similar goals,” Chow says, and describes the situation as a bit of a turf war.

So score one for IBM for releasing the first cloud service.

Here are some photos of the computer.

China retains supercomputer crown.


A supercomputer built by the Chinese government has retained its place at the top of a list of the world’s most powerful systems.

Tianhe-2 can operate at 33.86 petaflop/s – the equivalent of 33,863 trillion calculations per second – according to a test called the Linpack benchmark.

There was only one change near the top of the leader board.

Switzerland‘s new Piz Daint – with 6.27 petaflop/s – made sixth place.

The Top500 list is compiled twice-yearly by a team led by a professor from Germany’s University of Mannheim.

It measures how fast the computers can solve a special type of linear equation to determine their speed, but does not take account of other factors – such as how fast data can be transferred from one part of the system to another – which can also influence real-world performance.

Fastest supercomputers1. Tianhe-2 (China) 33.86 petaflop/sec2. Titan (US) 17.59 petaflop/sec3. Sequoia (US) 17.17 petaflop/sec4. K computer (Japan) 10.51 petaflop/sec

5. Mira (US) 8.59 petaflop/sec

6. Piz Daint (Swiss) 6.27 petaflop/sec

7. Stampede (US) 5.17 petaflop/sec

8. Juqueen (Germany) 5.09 petaflop/sec

9. Vulcan (US) 4.29 petaflop/sec

10. SuperMuc (Germany) 2.90 petaflop/sec

(Source: Top500 List based on Rmax Linpack benchmark)

IBM – which created five out of the 10 fastest supercomputers in the latest list – told the BBC it believed the way the list was calculated should now be updated, and would press for the change at a conference being held this week in Denver, Colorado.

“The Top500 has been a very useful tool in the past decades to try to have a single number that could be used to measure the performance and the evolution of high-performance computing,” said Dr Alessandro Curioni, head of the computational sciences department at IBM’s Zurich research lab.

“[But] today we need a more practical measurement that reflects the real use of these supercomputers based on their most important applications.

“We use supercomputers to solve real problems – to push science forward, to help innovation, and ultimately to make our lives better.

“So, one thing that myself and some of my colleagues will do is discuss with the Top500 organisers adding in new measurements.”

However, one of the list’s creators suggested the request would be denied.

“A very simple benchmark, like the Linpack, cannot reflect the reality of how many real application perform on today’s complex computer systems,” said Erich Strohmaier.

“More representative benchmarks have to be much more complex in their coding, their execution and how many aspects of their performance need to be recorded and published. This makes understanding their behaviour more difficult.

“Finding a good middle-ground between these extremes has proven to be very difficult, as unfortunately all previous attempts found critics from both camps and were not widely adopted.”

China’s lead

Tianhe-2 – which translates as Milky Way 2 – was developed by China’s National University of Defence Technology and will be based in the city of Guangzhou, in the country’s south-eastern Guandong province.

Fluid dynamics simulation
IBM’s Sequoia recently carried out a simulation of a cloud of 15,000 bubbles

It uses a mixture of processors made by Intel as well as custom-made CPUs (central processing units) designed by the university itself.

The system is to be offered as a “research and education” tool once tests are completed, with local reports suggesting that officials have picked the car industry as a “priority” client.

Its Linpack score is nearly double that of the next supercomputer in the list – Titan, the US Department of Energy’s system at the Oak Ridge National Laboratory in Tennessee.

However, one expert said it was still too early to know whether the Chinese system would be able to outperform its US counterpart in real-world tasks.

“You can get bottlenecks,” said Prof Alan Woodward, from the University of Surrey’s department of computing.

“Talking about the number of calculations that can be carried out per second isn’t the same as saying a supercomputer can do that in practice in a sustained way. The processors might be kicking their heels some of the time if they don’t get the data as fast as they can handle, for example.”

Energy efficiency

Supercomputer applications do not tend to use all the processor power on offer.

IBM notes that its own Sequoia supercomputer – which came third on the latest list – used a relatively high 73% of the machine’s theoretical peak performance when it recently carried out what the firm describes as the biggest ever fluid dynamics simulation to date.

Piz Daint supercomputer
Switzerland’s Piz Daint will be used to model galaxy formations and weather patterns

The test involved creating virtual equivalents of 15,000 collapsing bubbles – something researchers are studying to find new ways to destroy kidney stones and cancerous cells.

“The thing you want to avoid is to throw away resources,” reflected Dr Curioni.

“For scientists, the most important thing is how fast you solve a problem using the machine in an efficient way.

“When we run these types of simulations we invest much larger amounts of money running the machines than buying them.”

He added that one of the biggest costs involved is energy use.

According to the Top500 list, Tianhe-2 requires 17,808 of kW power – more than double the 8,209 kW needed by Titan or the 7,890 kW needed by Sequoia.

Dr Curioni believes a revised leader board should take energy efficiency into account.

But Prof Woodward agreed with the list’s creators that getting researchers and the governments that sponsored them to agree to a new methodology might be easier said than done.

“There is a lot of kudos in having what is termed the fastest supercomputer,” he said.

“So, there will be resistance to a definition that favours one computer over another.”

IBM’s Watson Now Tackles Clinical Trials At MD Anderson Cancer Center.


IBM continues to expand the use of its Watson supercomputer from winning Jeopardy to handling incoming call-center questions to guiding cancer doctors at Memorial Sloan Kettering to better diagnoses. Today it announced a new pilot program for Watson at Houston’s renowned MD Anderson Cancer Center. The institution has been trying out Watson for a little under a year in its leukemia practice as an expert advisor to the doctors running clinical trials for new drugs.

According to the FDA, some $95 billion is spent on clinical trials each year and only 6% are completed on time. Even at the best cancer centers, doctors running trials are feeling around in the dark. There are so many variables in play for each patient and clinicians traditionally only look at the few dozen they feel are the most important. Watson, fed with terabytes of general knowledge, medical literature and MD Anderson’s own electronic medical records, can riffle through thousands more variables to solve the arduous task of matching patients to the right trial and managing their progress on new cancer drugs.

“It’s still in testing and not quite ready for the mainstream yet, but it has the infrastructure to potentially revolutionize oncology research,” says MD Anderson cancer doctor Courtney DiNardo. “Just having all of a patient’s data immediately on one screen is a huge time saver. It used to take hours sometimes just to organize it all.”

In a blog post published today, Dr. DiNardo gave an example of filling in for a colleague who was out of town. She had to meet with one of his patients who had a particularly complicated condition that needed a management decision. “Under normal circumstances, it may have taken me all afternoon to prepare for the meeting with enough insight to provide the most appropriate treatment decisions. With Watson, I am able to get a patient’s history, characteristics, and treatment recommendations based on my patient’s unique characteristics in seconds.”

Watson isn’t taking over the work from doctors. It’s more of an advisor, giving evidence-based treatment advice based on standards of care, while providing the scientific rationale for each choice it makes. Doctors can click on any option and drill down to the medical literature or patient data used to generate that option, along with the level of confidence Watson has in that source. “It’s tracking everything and alerts you when something’s wrong, like when you need to start a patient on prophylactic antibiotics if they have severe neutropenia (an abnormally low number of a certain type of white blood cell),” said Dr. DiNardo. “We might know that here at MD Anderson because we see hundreds of leukemia patients, but a less expert center might not. Now everyone in the world becomes a leukemia expert.”

So far only ten oncologists on faculty at MD Anderson have been involved in the pilot and, while Watson is using real patient data, its advice hasn’t been applied to real patients yet. That might come in early 2014. But for DiNardo, just the Big Data opportunity alone of using Watson to look across all of genomics and molecular research while assimilating every structured and unstructured byte of patient data, is tantalizing. “The potential is really amazing,” she says.

Can wearable technology boost productivity?


 

With great power comes great responsibility. There is some confusion over whether this quote should be attributed to Voltaire or Spiderman.

Either way, the message is the same and one that should be resonating with the inventors, companies, brands, media, policy makers and industries hitching a ride on the innovation bullet train of wearable technologies.

 

Our original Human Cloud research project at Goldsmiths, University of London in partnership with cloud computing provider Rackspace focused on the socio-economic impact of wearable technology moving from novelty and entertainment to health and lifestyle.

We conducted a survey of 4,000 adults in the UK and US and spent six weeks with 26 participants experimenting with these new technologies, from fitness bands like the Fitbit, Jawbone Up and Nike Fuelband, to sensor-based wearable cameras like the Autographer.

 

With echoes of Stephen Hawking‘s voice on Radiohead‘s “OK Computer” album, participants experimenting with wearable technologies felt fitter (68%), happier (75%), and more productive (84%).

The nuances of the human experience was reflected in the six archetypes of wearable technology users we identified from deep qualitative research from the curious, controllers, and quantified selfers to the self-medics, finish line fanatics, and ubiquitors.

“As you can see, today has not gone well so far,” says one self-medic participant mournfully, looking at two graphs: one shows he only took 394 steps that day, the other that he only got five hours 28 minutes sleep. When asked why he wears technology, his answer is to “prevent delusion” and so that function is at least achieved.

Privacy remains a key issue, but it is a multifaceted and complex discussion.

Twenty percent of survey respondents wanted to see Google Glass banned entirely from public spaces, but the same percentage were willing to share the data from wearable devices with government to improve services.

 

The argument from our ‘controller’ archetype is that their data is already valuable, the question is who is benefiting and exploiting this value.

Fernando Pessoa wrote that it is the fate of everyone in this life to be exploited so is it worse to be exploited by Senhor Vasques [his employer] and his textile company than by vanity, glory, resentment, envy, or the impossible?

This is a question all of us must answer, particularly as the fine line between the possible and the seemingly impossible is breached nearly every day by one form of emerging technology or another fueled by the exponential growth of computing power, storage, bandwidth, nanotechnology, and big data.

One of the most intriguing findings of the initial phase of the research was the way early adopter companies were starting to explore the power of wearable tech in the workplace.

Several companies reported issuing laptops, mobile phones, and fitness bands to all employees as part of standard corporate kit. This stimulated our imagination and led to the next phase of our research now underway with Rackspace.

 

We are looking at a big data mash-up where the wearable tech human cloud meets the productivity and performance corporate cloud to amplify the role of the human cloud at work.

 

For businesses experimenting with these technologies there are implications for occupational psychology, systems development, insight and analytics, leadership, competitive advantage, environmental analysis and workplace design.

Three billion gigabytes of big data are generated every day, but only one-half of one percent of this data gets analyzed and put to work.

Wearable tech data from employees and customers are an inevitable key ingredient in the recipes for making sense of big data and the role of emerging technologies in shaping our cities, societies, markets and economies.

This big data stew can be augmented with cognitive and decision-support systems like IBM Watson, the computing service that famously triumphed on Jeopardy in 2011, now deployed in the cloud diagnosing and helping treat cancer patients.

With real-time access to human data in the workplace systems like Watson can potentially support specific decisions and scenarios in relation to your personal Human Cloud. We recognize it is not all about opportunities.

 

There are obvious surveillance implications and risks inherent in these kinds of dynamic data driven integrations of networks of people and systems.

Analysts at Credit Suisse suggest the wearable tech market will grow from $1.4bn (£878m) in annual sales this year to $50bn (£31.3bn) by 2018.

Your friendly neighborhood Spiderman also said some spiders change colors to blend into their environment. It’s a defense mechanism.

Wearable technologies are in the midst of this blending and soon will diffuse subtly but powerfully into the fabric of everyday lives so as to be unrecognizable as a distinct innovation domain.

At this stage it is the great responsibility of every one of us to consider those implications.

 

Computer made from tiny carbon tubes.


The first computer built entirely with carbon nanotubes has been unveiled, opening the door to a new generation of digital devices.

“Cedric” is only a basic prototype but could be developed into a machine which is smaller, faster and more efficient than today’s silicon models.

Nanotubes have long been touted as the heir to silicon’s throne, but building a working computer has proven awkward.

_70097569_maxshulaker_039

The breakthrough by Stanford University engineers is published in Nature.

Cedric is the most complex carbon-based electronic system yet realised.

So is it fast? Not at all. It might have been in 1955.

Cedric’s vital statistics

  • 1 bit processor
  • Speed: 1 kHz
  • 178 transistors
  • 10-200 nanotubes per transistor
  • 2 billion carbon atoms
  • Turing complete
  • Multitasking
  • 100 microns – width of human hair
  • 10 microns – water droplet
  • 8 microns – transistors in Cedric
  • 625 nanometres (nm) – wavelength of red light
  • 20-450 nm – single viruses
  • 22 nm latest silicon chips
  • 9 nm – smallest carbon nanotube chip
  • 6 nm – cell membrane
  • 1 nm – single carbon nanotube
  • _70097572_handholdingcntwafer

How small is a carbon computer chip?

The computer operates on just one bit of information, and can only count to 32.

“In human terms, Cedric can count on his hands and sort the alphabet. But he is, in the full sense of the word, a computer,” says co-author Max Shulaker.

“There is no limit to the tasks it can perform, given enough memory”.

In computing parlance, Cedric is “Turing complete”. In principle, it could be used to solve any computational problem.

It runs a basic operating system which allows it to swap back and forth between two tasks – for instance, counting and sorting numbers.

And unlike previous carbon-based computers, Cedric gets the answer right every time.

_70098463_handholdingcntwafer_008

Imperfection-immune

“People have been talking about a new era of carbon nanotube electronics, but there have been few demonstrations. Here is the proof,” said Prof Subhasish Mitra, lead author on the study.

The Stanford team hope their achievement will galvanise efforts to find a commercial successor to silicon chips, which could soon encounter their physical limits.

Carbon nanotubes (CNTs) are hollow cylinders composed of a single sheet of carbon atoms.

They have exceptional properties which make them ideal as a semiconductor material for building transistors, the on-off switches at the heart of electronics.

For starters, CNTs are so thin – thousands could fit side-by-side in a human hair – that it takes very little energy to switch them off.

“Think of it as stepping on a garden hose. The thinner the pipe, the easier it is to shut off the flow,” said HS Philip Wong, co-author on the study.

Continue reading the main story

How small is a carbon computer chip?

  • 100 microns – width of human hair
  • 10 microns – water droplet
  • 8 microns – transistors in Cedric
  • 625 nanometres (nm) – wavelength of red light
  • 20-450 nm – single viruses
  • 22 nm latest silicon chips
  • 9 nm – smallest carbon nanotube chip
  • 6 nm – cell membrane
  • 1 nm – single carbon nanotube

But while single-nanotube transistors have been around for 15 years, no-one had ever put the jigsaw pieces together to make a useful computing device.

So how did the Stanford team succeed where others failed? By overcoming two common bugbears which have bedevilled carbon computing.

First, CNTs do not grow in neat, parallel lines. “When you try and line them up on a wafer, you get a bowl of noodles,” says Mitra.

The Stanford team built chips with CNTs which are 99.5% aligned – and designed a clever algorithm to bypass the remaining 0.5% which are askew.

They also eliminated a second type of imperfection – “metallic” CNTs – a small fraction of which always conduct electricity, instead of acting like semiconductors that can be switched off.

To expunge these rogue elements, the team switched off all the “good” CNTs, then pumped the remaining “bad” ones full of electricity – until they vaporised. The result is a functioning circuit.

The Stanford team call their two-pronged technique “imperfection-immune design”. Its greatest trick? You don’t even have to know where the imperfections lie – you just “zap” the whole thing.

“These are initial necessary steps in taking carbon nanotubes from the chemistry lab to a real environment,” said Supratik Guha, director of physical sciences for IBM’s Thomas J Watson Research Center.

But hang on – what if, say, Intel, or another chip company, called up and said “I want a billion of these”. Could Cedric be scaled up and factory-produced?

In principle, yes: “There is no roadblock”, says Franz Kreupl, of the Technical University of Munich in Germany.

“If research efforts are focused towards a scaled-up (64-bit) and scaled-down (20-nanometre transistor) version of this computer, we might soon be able to type on one.”

Shrinking the transistors is the next challenge for the Stanford team. At a width of eight microns (8,000 nanometres) they are much fatter than today’s most advanced silicon chips.

But while it may take a few years to achieve this gold standard, it is now only a matter of time – there is no technological barrier, says Shulaker.

“In terms of size, IBM has already demonstrated a nine-nanometre CNT transistor.

“And as for manufacturing, our design is compatible with current industry processes. We used the same tools as Intel, Samsung or whoever.

“So the billions of dollars invested into silicon has not been wasted, and can be applied for CNTs.”

For 40 years we have been predicting the end of silicon. Perhaps that end is now in sight.

Source: BBC

Scientists create never-before-seen form of matter.


Harvard and MIT scientists are challenging the conventional wisdom about light, and they didn’t need to go to a galaxy far, far away to do it.

Working with colleagues at the Harvard-MIT Center for Ultracold Atoms, a group led by Harvard Professor of Physics Mikhail Lukin and MIT Professor of Physics Vladan Vuletic have managed to coax photons into binding together to form molecules – a state of matter that, until recently, had been purely theoretical. The work is described in a September 25 paper in Nature.

The discovery, Lukin said, runs contrary to decades of accepted wisdom about the nature of light. Photons have long been described as massless particles which don’t interact with each other – shine two laser beams at each other, he said, and they simply pass through one another.

“Photonic molecules,” however, behave less like traditional lasers and more like something you might find in science fiction – the light saber.

“Most of the properties of light we know about originate from the fact that photons are massless, and that they do not interact with each other,” Lukin said. “What we have done is create a special type of medium in which photons interact with each other so strongly that they begin to act as though they have mass, and they bind together to form molecules. This type of photonic bound state has been discussed theoretically for quite a while, but until now it hadn’t been observed.

“It’s not an in-apt analogy to compare this to light sabers,” Lukin added. “When these photons interact with each other, they’re pushing against and deflect each other. The physics of what’s happening in these molecules is similar to what we see in the movies.”

To get the normally-massless photons to bind to each other, Lukin and colleagues, including Harvard post-doctoral fellow Ofer Fisterberg, former Harvard doctoral student Alexey Gorshkov and MIT graduate students Thibault Peyronel and Qiu Liang couldn’t rely on something like the Force – they instead turned to a set of more extreme conditions.

Researchers began by pumped rubidium atoms into a vacuum chamber, then used lasers to cool the cloud of atoms to just a few degrees above absolute zero. Using extremely weak laser pulses, they then fired single photons into the cloud of atoms.

As the photons enter the cloud of cold atoms, Lukin said, its energy excites atoms along its path, causing the photon to slow dramatically. As the photon moves through the cloud, that energy is handed off from atom to atom, and eventually exits the cloud with the photon.

“When the photon exits the medium, its identity is preserved,” Lukin said. “It’s the same effect we see with refraction of light in a water glass. The light enters the water, it hands off part of its energy to the medium, and inside it exists as light and matter coupled together, but when it exits, it’s still light. The process that takes place is the same it’s just a bit more extreme – the light is slowed considerably, and a lot more energy is given away than during refraction.”

When Lukin and colleagues fired two photons into the cloud, they were surprised to see them exit together, as a single molecule.

The reason they form the never-before-seen molecules?

An effect called a Rydberg blockade, Lukin said, which states that when an atom is excited, nearby atoms cannot be excited to the same degree. In practice, the effect means that as two photons enter the atomic cloud, the first excites an atom, but must move forward before the second photon can excite nearby atoms.

The result, he said, is that the two photons push and pull each other through the cloud as their energy is handed off from one atom to the next.

“It’s a photonic interaction that’s mediated by the atomic interaction,” Lukin said. “That makes these two photons behave like a molecule, and when they exit the medium they’re much more likely to do so together than as single photons.”

While the effect is unusual, it does have some practical applications as well.

“We do this for fun, and because we’re pushing the frontiers of science,” Lukin said. “But it feeds into the bigger picture of what we’re doing because photons remain the best possible means to carry quantum information. The handicap, though, has been that photons don’t interact with each other.”

To build a quantum computer, he explained, researchers need to build a system that can preserve quantum information, and process it using quantum logic operations. The challenge, however, is that quantum logic requires interactions between individual quanta so that quantum systems can be switched to perform information processing.

“What we demonstrate with this process allows us to do that,” Lukin said. “Before we make a useful, practical quantum switch or photonic logic gate we have to improve the performance, so it’s still at the proof-of-concept level, but this is an important step. The physical principles we’ve established here are important.”

The system could even be useful in classical computing, Lukin said, considering the power-dissipation challenges chip-makers now face. A number of companies – including IBM – have worked to develop systems that rely on optical routers that convert light signals into electrical signals, but those systems face their own hurdles.

Lukin also suggested that the system might one day even be used to create complex three-dimensional structures – such as crystals – wholly out of light.

“What it will be useful for we don’t know yet, but it’s a new state of matter, so we are hopeful that new applications may emerge as we continue to investigate these photonic molecules’ properties,” he said.

 

 Source:  Nature