Down to the wire for silicon: Researchers create a wire four atoms wide, one atom tall

ScienceDaily (Jan. 5, 2012) — The smallest wires ever developed in silicon -- just one atom tall and four atoms wide -- have been shown by a team of researchers from the University of New South Wales, Melbourne University and Purdue University to have the same current-carrying capability as copper wires.

Experiments and atom-by-atom supercomputer models of the wires have found that the wires maintain a low capacity for resistance despite being more than 20 times thinner than conventional copper wires in microprocessors.

The discovery, which was published in this week's journal Science, has several implications, including:

For engineers it could provide a roadmap to future nanoscale computational devices where atomic sizes are at the end of Moore's law. The theory shows that a single dense row of phosphorus atoms embedded in silicon will be the ultimate limit of downscaling.For computer scientists, it places donor-atom based silicon quantum computing closer to realization.And for physicists, the results show that Ohm's Law, which demonstrates the relationship between electrical current, resistance and voltage, continues to apply all the way down to an atomic-scale wire.

Bent Weber, the paper's lead author and a graduate student in the Centre of Excellence for Quantum Computation and Communication Technology at the University of New South Wales, was thrilled with the finding.

"It's extraordinary to show that Ohm's Law, such a basic law, still holds even when constructing a wire from the fundamental building blocks of nature -- atoms," he says.

The innovation of the Australian group was to build the circuits up atom by atom, instead of the current method of building microprocessors, in which material is stripped away, says Gerhard Klimeck, a Purdue professor of electrical and computer engineering and director of the Network for Computational Nanotechnology.

"Typically we chip or etch material away, which can be very expensive, difficult and inaccurate," Klimeck says. "Once you get to 20 atoms wide you have atomic flucuations that make scaling difficult. But this experimental group built devices by placing atomically thin layers of phosphorus in silicon and found that with densely doped phosphorus wires just four atoms wide it acts like a wire that conducts just as well as metal."

The goal of the research is to develop future quantum computers in which single atoms are used for the computation, says Michelle Simmons, director of the Centre of Excellence for Quantum Computation and Communication Technology at the University of New South Wales and the project's principal investigator.

"We are on the threshold of making transistors out of individual atoms," Simmons says. "But to build a practical quantum computer we have recognized that the interconnecting wiring and circuitry also needs to shrink to the atomic scale."

Hoon Ryu, a Purdue graduate who is now a senior researcher with the Korea Institute of Science and Technology's Supercomputing Center, said the practicality of the research is exciting.

"The metallic wire is in principle quite difficult to be scaled into one- to two-nanometer pitch, but in both experimental and modeling views, the research result is quite remarkable," Ryu says. "For the first time, this demonstrates the possibility that densely doping wire is a viable alternative for the next-gerenation, ultra-scale metallic interconnect in silicon chips."

To assist the Australian researchers, Klimeck's research team ran hundreds of simulations to study the variability of these nanoscale structures.

"Having the throughput capability for a highly scalable code is important for doing that, and we have that capability here at Purdue with http://nanoHUB.org," Klimeck says. "We ran hundreds of cases to understand the potential landscape of these devices, so this was computationally intensive work."

Klimeck says that in addition to the project's scientific and engineering implications, he found the collaboration the most rewarding aspect.

"It is an exciting collaboration," he says. "We were doing simulations of experimental work, which was based on a theoretical model. So we were bringing the three legs of modern science together in one project. Plus, our graduate students are able to stay in contact and work with each other despite working in various locations around the world. It's hard to think of a better example of how science is done today."

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The above story is reprinted from materials provided by Purdue University. The original article was written by Steve Tally.

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Journal Reference:

B. Weber, S. Mahapatra, H. Ryu, S. Lee, A. Fuhrer, T. C. G. Reusch, D. L. Thompson, W. C. T. Lee, G. Klimeck, L. C. L. Hollenberg, M. Y. Simmons. Ohm's Law Survives to the Atomic Scale. Science, 2012; 335 (6064): 64-67 DOI: 10.1126/science.1214319

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Mimicking the brain -- in silicon: New computer chip models how neurons communicate with each other at synapses

ScienceDaily (Nov. 15, 2011) — For decades, scientists have dreamed of building computer systems that could replicate the human brain's talent for learning new tasks.

MIT researchers have now taken a major step toward that goal by designing a computer chip that mimics how the brain's neurons adapt in response to new information. This phenomenon, known as plasticity, is believed to underlie many brain functions, including learning and memory.

With about 400 transistors, the silicon chip can simulate the activity of a single brain synapse -- a connection between two neurons that allows information to flow from one to the other. The researchers anticipate this chip will help neuroscientists learn much more about how the brain works, and could also be used in neural prosthetic devices such as artificial retinas, says Chi-Sang Poon, a principal research scientist in the Harvard-MIT Division of Health Sciences and Technology.

Poon is the senior author of a paper describing the chip in the Proceedings of the National Academy of Sciences the week of Nov. 14. Guy Rachmuth, a former postdoc in Poon's lab, is lead author of the paper. Other authors are Mark Bear, the Picower Professor of Neuroscience at MIT, and Harel Shouval of the University of Texas Medical School.

Modeling synapses

There are about 100 billion neurons in the brain, each of which forms synapses with many other neurons. A synapse is the gap between two neurons (known as the presynaptic and postsynaptic neurons). The presynaptic neuron releases neurotransmitters, such as glutamate and GABA, which bind to receptors on the postsynaptic cell membrane, activating ion channels. Opening and closing those channels changes the cell's electrical potential. If the potential changes dramatically enough, the cell fires an electrical impulse called an action potential.

All of this synaptic activity depends on the ion channels, which control the flow of charged atoms such as sodium, potassium and calcium. Those channels are also key to two processes known as long-term potentiation (LTP) and long-term depression (LTD), which strengthen and weaken synapses, respectively.

The MIT researchers designed their computer chip so that the transistors could mimic the activity of different ion channels. While most chips operate in a binary, on/off mode, current flows through the transistors on the new brain chip in analog, not digital, fashion. A gradient of electrical potential drives current to flow through the transistors just as ions flow through ion channels in a cell.

"We can tweak the parameters of the circuit to match specific ion channels," Poon says. "We now have a way to capture each and every ionic process that's going on in a neuron."

Previously, researchers had built circuits that could simulate the firing of an action potential, but not all of the circumstances that produce the potentials. "If you really want to mimic brain function realistically, you have to do more than just spiking. You have to capture the intracellular processes that are ion channel-based," Poon says.

The new chip represents a "significant advance in the efforts to incorporate what we know about the biology of neurons and synaptic plasticity onto CMOS [complementary metal-oxide-semiconductor] chips," says Dean Buonomano, a professor of neurobiology at the University of California at Los Angeles, adding that "the level of biological realism is impressive.

The MIT researchers plan to use their chip to build systems to model specific neural functions, such as the visual processing system. Such systems could be much faster than digital computers. Even on high-capacity computer systems, it takes hours or days to simulate a simple brain circuit. With the analog chip system, the simulation is even faster than the biological system itself.

Another potential application is building chips that can interface with biological systems. This could be useful in enabling communication between neural prosthetic devices such as artificial retinas and the brain. Further down the road, these chips could also become building blocks for artificial intelligence devices, Poon says.

Debate resolved

The MIT researchers have already used their chip to propose a resolution to a longstanding debate over how LTD occurs.

One theory holds that LTD and LTP depend on the frequency of action potentials stimulated in the postsynaptic cell, while a more recent theory suggests that they depend on the timing of the action potentials' arrival at the synapse.

Both require the involvement of ion channels known as NMDA receptors, which detect postsynaptic activation. Recently, it has been theorized that both models could be unified if there were a second type of receptor involved in detecting that activity. One candidate for that second receptor is the endo-cannabinoid receptor.

Endo-cannabinoids, similar in structure to marijuana, are produced in the brain and are involved in many functions, including appetite, pain sensation and memory. Some neuroscientists had theorized that endo-cannabinoids produced in the postsynaptic cell are released into the synapse, where they activate presynaptic endo-cannabinoid receptors. If NMDA receptors are active at the same time, LTD occurs.

When the researchers included on their chip transistors that model endo-cannabinoid receptors, they were able to accurately simulate both LTD and LTP. Although previous experiments supported this theory, until now, "nobody had put all this together and demonstrated computationally that indeed this works, and this is how it works," Poon says.

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The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Anne Trafton, MIT News Office.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Note: If no author is given, the source is cited instead.

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Mimicking the brain -- in silicon: New computer chip models how neurons communicate with each other at synapses

ScienceDaily (Nov. 15, 2011) — For decades, scientists have dreamed of building computer systems that could replicate the human brain's talent for learning new tasks.

MIT researchers have now taken a major step toward that goal by designing a computer chip that mimics how the brain's neurons adapt in response to new information. This phenomenon, known as plasticity, is believed to underlie many brain functions, including learning and memory.

With about 400 transistors, the silicon chip can simulate the activity of a single brain synapse -- a connection between two neurons that allows information to flow from one to the other. The researchers anticipate this chip will help neuroscientists learn much more about how the brain works, and could also be used in neural prosthetic devices such as artificial retinas, says Chi-Sang Poon, a principal research scientist in the Harvard-MIT Division of Health Sciences and Technology.

Poon is the senior author of a paper describing the chip in the Proceedings of the National Academy of Sciences the week of Nov. 14. Guy Rachmuth, a former postdoc in Poon's lab, is lead author of the paper. Other authors are Mark Bear, the Picower Professor of Neuroscience at MIT, and Harel Shouval of the University of Texas Medical School.

Modeling synapses

There are about 100 billion neurons in the brain, each of which forms synapses with many other neurons. A synapse is the gap between two neurons (known as the presynaptic and postsynaptic neurons). The presynaptic neuron releases neurotransmitters, such as glutamate and GABA, which bind to receptors on the postsynaptic cell membrane, activating ion channels. Opening and closing those channels changes the cell's electrical potential. If the potential changes dramatically enough, the cell fires an electrical impulse called an action potential.

All of this synaptic activity depends on the ion channels, which control the flow of charged atoms such as sodium, potassium and calcium. Those channels are also key to two processes known as long-term potentiation (LTP) and long-term depression (LTD), which strengthen and weaken synapses, respectively.

The MIT researchers designed their computer chip so that the transistors could mimic the activity of different ion channels. While most chips operate in a binary, on/off mode, current flows through the transistors on the new brain chip in analog, not digital, fashion. A gradient of electrical potential drives current to flow through the transistors just as ions flow through ion channels in a cell.

"We can tweak the parameters of the circuit to match specific ion channels," Poon says. "We now have a way to capture each and every ionic process that's going on in a neuron."

Previously, researchers had built circuits that could simulate the firing of an action potential, but not all of the circumstances that produce the potentials. "If you really want to mimic brain function realistically, you have to do more than just spiking. You have to capture the intracellular processes that are ion channel-based," Poon says.

The new chip represents a "significant advance in the efforts to incorporate what we know about the biology of neurons and synaptic plasticity onto CMOS [complementary metal-oxide-semiconductor] chips," says Dean Buonomano, a professor of neurobiology at the University of California at Los Angeles, adding that "the level of biological realism is impressive.

The MIT researchers plan to use their chip to build systems to model specific neural functions, such as the visual processing system. Such systems could be much faster than digital computers. Even on high-capacity computer systems, it takes hours or days to simulate a simple brain circuit. With the analog chip system, the simulation is even faster than the biological system itself.

Another potential application is building chips that can interface with biological systems. This could be useful in enabling communication between neural prosthetic devices such as artificial retinas and the brain. Further down the road, these chips could also become building blocks for artificial intelligence devices, Poon says.

Debate resolved

The MIT researchers have already used their chip to propose a resolution to a longstanding debate over how LTD occurs.

One theory holds that LTD and LTP depend on the frequency of action potentials stimulated in the postsynaptic cell, while a more recent theory suggests that they depend on the timing of the action potentials' arrival at the synapse.

Both require the involvement of ion channels known as NMDA receptors, which detect postsynaptic activation. Recently, it has been theorized that both models could be unified if there were a second type of receptor involved in detecting that activity. One candidate for that second receptor is the endo-cannabinoid receptor.

Endo-cannabinoids, similar in structure to marijuana, are produced in the brain and are involved in many functions, including appetite, pain sensation and memory. Some neuroscientists had theorized that endo-cannabinoids produced in the postsynaptic cell are released into the synapse, where they activate presynaptic endo-cannabinoid receptors. If NMDA receptors are active at the same time, LTD occurs.

When the researchers included on their chip transistors that model endo-cannabinoid receptors, they were able to accurately simulate both LTD and LTP. Although previous experiments supported this theory, until now, "nobody had put all this together and demonstrated computationally that indeed this works, and this is how it works," Poon says.

Recommend this story on Facebook, Twitter,
and Google +1:

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Story Source:

The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Anne Trafton, MIT News Office.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here