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Berkeley Lab announces first transistor with a working 1-nanometer gate

Schematic of a transistor with molybdenum disulfide semiconductor and 1-nanometer carbon nanotube gate. (credit: Sujay Desai/Berkeley Lab)

The first transistor with a working 1-nanometer (nm) gate* has been created by a team led by Lawrence Berkeley National Laboratory (Berkeley Lab) scientists. Until now, a transistor gate size less than 5 nanometers has been considered impossible because of quantum tunneling effects. (One nanometer is the diameter of a glucose molecule.)

The breakthrough was achieved by creating a 2D (flat) semiconductor field-effect transistor using molybdenum disulfide (MoS2) instead of silicon and a 1D single-walled carbon nanotube (SWCNT) as a gate electrode, instead of various metals. (SWCNTs are hollow cylindrical tubes with diameters as small as 1 nanometer.)

The MoS2 advantage

Compared with MoS2, electrons flowing through silicon are lighter and encounter less resistance . But with a gate length below 5 nanometers in length, a quantum mechanical phenomenon called tunneling kicks in, and the gate barrier is no longer able to keep the electrons from barging through from the source to the drain terminals, so the transistor cannot be turned off.

Electrons flowing through MoS2 are heavier, so their flow can be controlled with smaller gate lengths. MoS2 can also be scaled down to atomically thin sheets, about 0.65 nanometers thick, with a a larger band gap and lower dielectric constant, a measure reflecting the ability of a material to store energy in an electric field (similar to a capacitor). These properties help improve the control of the flow of current inside the transistor when the gate length is reduced to 1 nanometer.

Transistors consist of three terminals: a source (left), a drain (right), and a gate (the carbon nanotube, black, below). Current flows through the semiconductor (MoS2, represented by the yellow molecular model) from the source to the drain. Based on the voltage applied to the gate, it switches the channel (the portion of the MoS2 semiconductor just above the carbon nanotube) on and off, via a dielectric (zirconium oxide, green), operating in a manner similar to a capacitor. (credit: Sujay Desai/Berkeley Lab)

“We made the smallest transistor reported to date,” said faculty scientist Ali Javey at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and lead principal investigator of the Electronic Materials program in Berkeley Lab’s Materials Science Division. “The gate length is considered a defining dimension of the transistor. We demonstrated a 1-nanometer-gate transistor, showing that with the choice of proper materials, there is a lot more room to shrink our electronics.”

The development could be key to keeping alive Intel co-founder Gordon Moore’s prediction that the density of transistors on integrated circuits would double every two years, enabling the increased performance of our laptops, mobile phones, televisions, and other electronics.

“The semiconductor industry has long assumed that any gate below 5 nanometers wouldn’t work, so anything below that was not even considered,” said study lead author Sujay Desai, a graduate student in Javey’s lab. “This research shows that sub-5-nanometer gates should not be discounted. Industry has been squeezing every last bit of capability out of silicon. By changing the material from silicon to MoS2, we can make a transistor with a gate that is just 1 nanometer in length, and operate it like a switch.”

Transmission electron microscope image of a cross section of the transistor, showing the edge of a 1-nanometer carbon nanotube gate and the molybdenum disulfide semiconductor separated by zirconium dioxide, which is a dielectric insulator. (credit: Sujay B. Desai/Science)

Continuing Moore’s law

“This work demonstrated the shortest transistor ever,” said Javey, who is also a UC Berkeley professor of electrical engineering and computer sciences. “However, it’s a proof of concept. We have not yet packed these transistors onto a chip, and we haven’t done this billions of times over. We also have not developed self-aligned fabrication schemes for reducing parasitic resistances in the device. But this work is important to show that we are no longer limited to a 5-nanometer gate for our transistors. Moore’s Law can continue a while longer by proper engineering of the semiconductor material and device architecture.”

The findings appeared in the Oct. 7 issue of the journal Science. Researchers at the University of Texas at Dallas, Stanford University, and  the University of California, Berkeley, were also involved. The work at Berkeley Lab was primarily funded by the Department of Energy’s Basic Energy Sciences program.

According to an earlier article in CTimes on Sept. 30, Taiwan Semiconductor Manufacturing Co., Ltd. (TSMC) said the company is working toward a 1-nanometer manufacturing process, starting with a “5 nanometers process technology, while putting about 300 to 400 R&D personnel in developing more advanced 3-nanometer process.” However, TSMC spokesperson Elizabeth Sun told KurzweilAI that “no further information regarding any technology either under development or in path-finding stage will be disclosed to the public at this point.”

* Gate length is the length of the gate portion of the transistor, not to be confused with “node,” which was initially a measure of “half pitch” (half of the distance between features of a transistor), but the number itself has lost the exact meaning it once held. Gate length was 26nm for the 22nm node from Intel and 20 nanometers for the more recent 14nm node from Intel. — S. Natarajan et al., “A 14nm logic technology featuring 2nd-generation FinFET, air-gapped interconnects, self-aligned double patterning and a 0.0588 µm2 SRAM cell size,” 2014 IEEE International Electron Devices Meeting, San Francisco, CA, 2014, pp. 3.7.1-3.7.3. doi: 10.1109/IEDM.2014.7046976

Abstract of MoS2 transistors with 1-nanometer gate lengths

Scaling of silicon (Si) transistors is predicted to fail below 5-nanometer (nm) gate lengths because of severe short channel effects. As an alternative to Si, certain layered semiconductors are attractive for their atomically uniform thickness down to a monolayer, lower dielectric constants, larger band gaps, and heavier carrier effective mass. Here, we demonstrate molybdenum disulfide (MoS2) transistors with a 1-nm physical gate length using a single-walled carbon nanotube as the gate electrode. These ultrashort devices exhibit excellent switching characteristics with near ideal subthreshold swing of ~65 millivolts per decade and an On/Off current ratio of ~106. Simulations show an effective channel length of ~3.9 nm in the Off state and ~1 nm in the On state.

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IBM announces AI-powered decision-making

Project DataWorks predictive model (credit: IBM)

IBM today announced today Watson-based “Project DataWorks,” the first cloud-based data and analytics platform to integrate all types of data and enable AI-powered decision-making.

Project DataWorks is designed to make it simple for business leaders and data professionals to collect, organize, govern, and secure data, and become a “cognitive business.”

Achieving data insights is increasingly complex, and most of this work is done by highly skilled data professionals who work in silos with disconnected tools and data services that may be difficult to manage, integrate, and govern, says IBM. Businesses must also continually iterate their data models and products — often manually — to benefit from the most relevant, up-to-date insights.

IBM says Project DataWorks can help businesses break down these barriers by connecting all data and insights for their users into an integrated, self-service platform.

Available on Bluemix, IBM’s Cloud platform, Project DataWorks is designed to help organizations:

  • Automate the deployment of data assets and products using cognitive-based machine learning and Apache Spark;
  • Ingest data faster than any other data platform, from 50 to hundreds of Gbps, and all endpoints: enterprise databases, Internet of Things, weather, and social media;
  • Leverage an open ecosystem of more than 20 partners and technologies, such as Confluent, Continuum Analytics, Galvanize, Alation, NumFOCUS, RStudio, Skymind, and more.

 

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D-Wave Systems previews 2000-qubit quantum processor

D-Wave 2000-qubit processor (credit: D-Wave Systems)

D-Wave Systems announced Tuesday (Sept. 28, 2016) a new 2000-qubit processor, doubling the number of qubits over the previous-generation D-Wave 2X system. The new system will enable larger problems to be solved and performance improvements of up to 1000 times.

D-Wave’s quantum system runs a quantum-annealing algorithm to find the lowest points in a virtual energy landscape representing a computational problem to be solved. The lowest points in the landscape correspond to optimal or near-optimal solutions to the problem. The increase in qubit count enables larger and more difficult problems to be solved, and the ability to tune the rate of annealing of individual qubits will enhance application performance.

According to D-Wave, users will be able to tune the quantum computational process to solve problems faster and find more diverse solutions when they exist. They will have the ability to sample the state of the quantum computer during the quantum annealing process, which will power hybrid quantum-classical machine learning algorithms that were not possible before.

The system will also allow for combining quantum processing with classical processing, improving the quality of optimization and sampling results returned from the system.

D-Wave’s first users conference, being held on September 28–29 in Santa Fe, New Mexico, features speakers from Los Alamos National Laboratory, NASA, Lockheed Martin, the Roswell Park Cancer Center, Oak Ridge National Laboratory, USC, and D-Wave, and a number of quantum software and services companies.

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Someone is learning how to take down the Internet

Submarine cables map (credit: Teleography)

“Over the past year or two, someone has been probing the defenses of the companies that run critical pieces of the Internet,” according to a blog post by security expert Bruce Schneier.

“These probes take the form of precisely calibrated attacks designed to determine exactly how well these companies can defend themselves, and what would be required to take them down. It feels like a nation’s military cybercommand trying to calibrate its weaponry in the case of cyberwar.”

Schneier said major companies that provide the basic infrastructure that makes the Internet work [presumably, ones such as Cisco] have seen an increase in distributed denial of service (DDoS) attacks against them, and the attacks are significantly larger, last longer, and are more sophisticated.

“They look like probing — being forced to demonstrate their defense capabilities for the attacker.” This is similar to flying reconnaissance planes over a country to detect capabilities by making the enemy turn on air-defense radars.

Who might do this? “The size and scale of these probes — and especially their persistence — point to state actors. … China or Russia would be my first guesses.”

 

 

 

 

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Self-powered ‘materials that compute’ and recognize simple patterns

Conceptual illustration of pattern recognition process performed by hybrid gel-piezoelectric oscillator system (credit: Yan Fang)

University of Pittsburgh researchers have modeled the design of a “material that computes” — a hybrid material, powered only by its own chemical reactions, that can recognize simple patterns.

The material could one day be integrated into clothing and used to monitor the human body, or developed as a skin for “squishy” robots, for example, according to the researchers, writing in the open-access AAAS journal Science Advances.

A computer that combines gels and piezeoelectric materials

The computations (needed to design the hypothetical material) were modeled utilizing Belousov-Zhabotinsky (BZ) gels, a substance that oscillates in the absence of external stimuli, combined with an overlaying piezoelectric (PZ) cantilever, forming “BZ-PZ” (as in “easy peasy”). The BZ gels oscillate periodically, triggered by chemical stimulation, without the need for external driving stimuli. Piezoelectric (PZ) materials generate a voltage when deformed and, conversely, undergo deformation in the presence of an applied voltage.

Two BZ-PZ oscillator units connected with electrical wires. Triggered by the chemical oscillations, the BZ gels (green) expand in volume, generating a force (F1 and F2) and thereby cause the deflections ξ1 and ξ2 of the PZ cantilevers (orange and blue layers) , which generate an electric voltage U. That voltage then deflects the cantilevers (the inverse PZ effect), which then compress the underlying BZ gels and thereby modify the chemomechanical oscillations in these gels. The end result is the components’ response to self-generated signals (sensing), volumetric changes in the gel (actuation), and the passage of signals between the units (communication). For computation, the communication also leads to synchronization of the BZ gel oscillators. (credit: Yan Fang et al./Science Advances)

“By combining these attributes into a ‘BZ-PZ’ unit and then connecting the units by electrical wires, we designed a device that senses, actuates, and communicates without an external electrical power source,” the researchers explain in the paper.*

The result is that the device can also be used to perform computation. To use that for pattern recognition, the researchers first stored a pattern of numbers as a set of polarities in the BZ-PZ units, and the input patterns were coded with the initial phase of the oscillations imposed on these units.

Multiple BZ-PS units wired in serial and parallel configurations to form a network (credit: Yan Fang et al./Science Advances)

With multiple BZ-PZ units, the oscillators can be wired into a network  formed, for example, from units that are connected in parallel or in series. The resulting transduction between chemomechanical and electrical energy creates signals that quickly propagate and thus permits remote coupled oscillators to communicate and synchronize. This synchronization behavior in BZ-PZ network can be used for oscillator-based computing.

The computational modeling revealed that the input pattern closest to the stored pattern exhibits the fastest convergence time to the stable synchronization behavior, and is the most effective at recognizing patterns. In this study, the materials were programmed to recognize black-and-white pixels in the shape of numbers that had been distorted.

The researchers’ next goal is to expand from analyzing black-and-white pixels to grayscale and more complicated images and shapes, as well as to enhance the devices storage capability.

Perfect for monitoring human and robot bodies

Compared to a traditional computer, these computations are slow and take minutes. “Individual events are slow because the period of the BZ oscillations is slow,” said Victor V. Yashin, Research Assistant Professor of Chemical and Petroleum Engineering. “However, there are some tasks that need a longer analysis, and are more natural in function. That’s why this type of system is perfect to monitor environments like the human body.”

For example, Dr. Yashin said that patients recovering from a hand injury could wear a glove that monitors movement, and can inform doctors whether the hand is healing properly or if the patient has improved mobility. Another use would be to monitor individuals at risk for early onset Alzheimer’s, by wearing footwear that would analyze gait and compare results against normal movements, or a garment that monitors cardiovascular activity for people at risk of heart disease or stroke.

Since the devices convert chemical reactions to electrical energy, there would be no need for external electrical power. This would also be ideal for a robot or other device that could utilize the material as a sensory skin.

The research is funded by a five-year National Science Foundation Integrated NSF Support Promoting Interdisciplinary Research and Education (INSPIRE) grant, which focuses on complex and pressing scientific problems that lie at the intersection of traditional disciplines.

“This work at the University of Pittsburgh … is an example of this groundbreaking shift away from traditional silicon CMOS-based digital computing to a non-von Neumann machine in a polymer substrate, with remarkable low power consumption,” said Sankar Basu, NSF program director.

* This continues the research of Anna C. Balazs, Distinguished Professor of Chemical and Petroleum Engineering, and Steven P. Levitan, the John A. Jurenko Professor of Electrical and Computer Engineering. 

Abstract of Pattern recognition with “materials that compute”

Driven by advances in materials and computer science, researchers are attempting to design systems where the computer and material are one and the same entity. Using theoretical and computational modeling, we design a hybrid material system that can autonomously transduce chemical, mechanical, and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemomechanical oscillations of the BZ gels deflect the PZ layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, where the mode of synchronization depends on the polarity of the PZ. We show that the network of coupled, synchronizing BZ-PZ oscillators can perform pattern recognition. The “stored” patterns are set of polarities of the individual BZ-PZ units, and the “input” patterns are coded through the initial phase of the oscillations imposed on these units. The results of the modeling show that the input pattern closest to the stored pattern exhibits the fastest convergence time to stable synchronization behavior. In this way, networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating “materials that compute.”

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Harvard, Caltech design mechanical signaling, diodes, logic gates for soft robots

The Harvard/Caltech system for transmitting a mechanical signal consists of a series of bistable elements (the vertical beam, d, shown here) connected by soft coupling elements (wiggly lines), with two stable states. (Top) When a beam is displaced (by amount x), it stores energy. (Bottom) When it snaps back, it releases that stored energy into the coupling element on the right, which continues down the line, like dominos. (Scale bars represent 5 mm.) (credit: Jordan R. Raney/PNAS)

A new way to send mechanical signals through soft robots and other autonomous soft systems has been developed by researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with colleagues at the California Institute of Technology, described in the journal Proceedings of the National Academy of Sciences.

Soft autonomous systems, just like the human body, can perform delicate movements that are safe for humans, unlike mechanical actuators controlled by wires. The problem is that in sending a mechanical signal through a soft material — to make a robot “muscle” move, for example — the signal becomes dissipated (weakened) and dispersed (scattered).

Think tapping on a solid wall to communicate via morse code with someone in the next room vs. tapping out a muffled message on a wall covered with thick, soft foam.

Transmitting signals through soft materials

The researchers solved this problem by using “bistable beams” (structures that function in two distinct states) to store and release elastic energy along the path of a wave.

This new system consists of a chain of bistable elastomeric (rubber-like) beam structures connected by elastomeric linear springs. When a beam is deformed (bent), it snaps and stores energy. As the signal travels along the elastomer, it snaps the beam back into place, releasing the beam’s stored energy and sending the signal downstream, like a line of dominos. This simple bistable system prevents the signal from dissipating downstream.

“This design solves two fundamental problems in transmitting information through materials,” said Katia Bertoldi, the John L. Loeb Associate Professor of the Natural Sciences at SEAS and senior author of the paper.  “It not only overcomes dissipation, but it also eliminates dispersive [spreading out] effects, so that the signal propagates without distortion.  As such, we maintain signal strength and clarity from start to end.” The team used advanced 3D printing techniques to fabricate the system.

Soft diodes and logic gates

(A) A bifurcated (split into two) signal chain demonstrating tunable logic in a soft mechanical system. The distance d(out) determines the logical behavior, producing either an AND or an OR gate from the same system. (B) When d(out) is small (in this case, 16.7 mm) the energy barrier is higher, so both input signals must be strong to enable the wave to propagate through the output — a logical AND gate; (C) By increasing d(out) (to 18.6 mm in this case), the energy barrier decreases, producing a logical OR gate; in which case, either (or both) input signal has sufficient energy to trigger an output signal. (credit: Jordan R. Raney/PNAS)

The team also took the system a step further, designing and 3D-printing soft diodes and logic gates (a basic computational element that is normally part of a computer chip) using this same signal-transmission design. The gate can be controlled to act either as an AND (both inputs must be present to trigger the gate to fire) or as an OR gate (either one or both will trigger the gate to fire).

This research was supported by the National Science Foundation and the Harvard University Materials Research Science and Engineering Center (MRSEC).

Abstract of Stable propagation of mechanical signals in soft media using stored elastic energy

Soft structures with rationally designed architectures capable of large, nonlinear deformation present opportunities for unprecedented, highly-tunable devices and machines. However, the highly-dissipative nature of soft materials intrinsically limits or prevents certain functions, such as the propagation of mechanical signals. Here, we present an architected soft system comprised of elastomeric bistable beam elements connected by elastomeric linear springs. The dissipative nature of the polymer readily damps linear waves, preventing propagation of any mechanical signal beyond a short distance, as expected. However, the unique architecture of the system enables propagation of stable, nonlinear solitary transition waves with constant, controllable velocity and pulse geometry over arbitrary distances. Since the high damping of the material removes all other linear, small amplitude excitations, the desired pulse propagates with high delity and controllability. This phenomenon can be used to control signals, as demonstrated by the design of soft mechanical diodes and logic gates.

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World’s smallest storage device writes information atom by atom

STM scan (96 nm wide, 126 nm tall) of the 1 kB memory, written to a section of Feynman’s lecture, “There’s Plenty of Room at the Bottom” (credit: TU Delft/Ottelab)

Scientists at Kavli Institute of Nanoscience at Delft University have built a nanoscale data storage device containing 1 kilobyte (8,000 bits) with a storage density of 500 terabits per square inch (Tbpsi) — 500 times denser than the best commercial hard disk drive currently available. Each bit is represented by the position of one single chlorine atom.

“In theory, this storage density would allow all books ever created by humans to be written on a single post stamp,” says lead scientist Sander Otte. The research is reported today (Monday July 18) in Nature Nanotechnology.

Every day, modern society creates more than a billion gigabytes of new data. To store all this data, it is increasingly important that each single bit occupies as little space as possible.

In 1959, physicist Richard Feynman challenged his colleagues to engineer the world at the smallest possible scale. In his famous lecture There’s Plenty of Room at the Bottom, he speculated that if we had a platform allowing us to arrange individual atoms in an exact orderly pattern, it would be possible to store one piece of information per atom. To honor the visionary Feynman, Otte and his team have coded a section of Feynman’s lecture on an area 100 nanometers wide.

“Sliding puzzle” scheme

Atomic data storage scheme (credit: Kavli Institute of Nanoscience)

The team used a scanning tunneling microscope (STM), in which a sharp needle probes the atoms of a surface, one by one. With these probes scientists can see atoms and push them around. “You could compare it  to a sliding puzzle,” Otte explains. “Every bit consists of two positions on a surface of copper atoms, and one chlorine atom that we can slide back and forth between these two positions. If the chlorine atom is in the top position, there is a hole beneath it — we call this a 1. If the hole is in the top position and the chlorine atom is therefore on the bottom, then the bit is a 0.”

Because the chlorine atoms are surrounded by other chlorine atoms, except near the holes, they keep each other in place. Which is why this method with holes is much more stable than methods with loose atoms and more suitable for data storage.

Kilobyte atomic memory. 1,016-byte atomic memory, written to a passage from Feynman’s lecture, “There’s plenty of room at the bottom.” The memory consists of 127 functional blocks and 17 broken blocks, resulting in an overall areal density of 0.778 bits per nm square. (credit: F. E. Kalff et al./Nature Nanotechnology)

The researchers organized their memory in blocks of 8 bytes (64 bits). Each block has a marker, made of the same type of “holes” as the raster of chlorine atoms. Inspired by the pixelated square barcodes (QR codes) often used to scan tickets for airplanes and concerts, these markers work like miniature QR codes that carry information about the precise location of the block on the copper layer. The code will also indicate if a block is damaged, for instance due to some local contaminant or an error in the surface. This allows the memory to be scaled up easily to very big sizes, even if the copper surface is not entirely perfect.

The new approach offers excellent prospects in terms of stability and scalability. However, “in its current form the memory can operate only in very clean vacuum conditions and at liquid nitrogen temperature (77 K), so the actual storage of data on an atomic scale is still some way off.”

This research was support by the Netherlands Organisation for Scientific Research (NOW/FOM). Scientists of the International Iberian Nanotechnology Laboratory (INL) in Portugal performed calculations on the behavior of the chlorine atoms.


Delft University of Technology | Atomic scale data storage

Abstract of A kilobyte rewritable atomic memory

The advent of devices based on single dopants, such as the single-atom transistor, the single-spin magnetometer and the single-atom memory, has motivated the quest for strategies that permit the control of matter with atomic precision. Manipulation of individual atoms by low-temperature scanning tunnelling microscopy provides ways to store data in atoms, encoded either into their charge state, magnetization state or lattice position. A clear challenge now is the controlled integration of these individual functional atoms into extended, scalable atomic circuits. Here, we present a robust digital atomic-scale memory of up to 1 kilobyte (8,000 bits) using an array of individual surface vacancies in a chlorine-terminated Cu(100) surface. The memory can be read and rewritten automatically by means of atomic-scale markers and offers an areal density of 502 terabits per square inch, outperforming state-of-the-art hard disk drives by three orders of magnitude. Furthermore, the chlorine vacancies are found to be stable at temperatures up to 77 K, offering the potential for expanding large-scale atomic assembly towards ambient conditions.

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The top 10 emerging technologies of 2016

(credit: WEF)

The World Economic Forum’s annual list of this year’s breakthrough technologies, published today, includes “socially aware” openAI, grid-scale energy storage, perovskite solar cells, and other technologies with the potential to “transform industries, improve lives, and safeguard the planet.” The WEF’s specific interest is to “close gaps in investment and regulation.”

“Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors. The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies,” said Bernard Meyerson, PhD, Chief Innovation Officer of IBM and Chair of the WEF’s Meta-Council on Emerging Technologies.

The list also provides an opportunity to debate human, societal, economic or environmental risks and concerns that the technologies may pose — prior to widespread adoption.

One of the criteria used by council members during their deliberations was the likelihood that 2016 represents a tipping point in the deployment of each technology. So the list includes some technologies that have been known for a number of years, but are only now reaching a level of maturity where their impact can be meaningfully felt.

The top 10 technologies that make this year’s list are:

  1. Nanosensors and the Internet of Nanothings  — With the Internet of Things expected to comprise 30 billion connected devices by 2020, one of the most exciting areas of focus today is now on nanosensors capable of circulating in the human body or being embedded in construction materials. They could use DNA and proteins to recognize specific chemical targets, store a few bits of information, and then report their status by changing color or emitting some other easily detectable signal.
  2. Next-Generation Batteries — One of the greatest obstacles holding renewable energy back is matching supply with demand, but recent advances in energy storage using sodium, aluminum, and zinc based batteries makes mini-grids feasible that can provide clean, reliable, around-the-clock energy sources to entire villages.
  3. The Blockchain — With venture investment related to the online currency Bitcoin exceeding $1 billion in 2015 alone, the economic and social impact of blockchain’s potential to fundamentally change the way markets and governments work is only now emerging.
  4. 2D Materials — Plummeting production costs mean that 2D materials like graphene are emerging in a wide range of applications, from air and water filters to new generations of wearables and batteries.
  5. Autonomous Vehicles — The potential of self-driving vehicles for saving lives, cutting pollution, boosting economies, and improving quality of life for the elderly and other segments of society has led to rapid deployment of key technology forerunners along the way to full autonomy.
  6. Organs-on-chips — Miniature models of human organs could revolutionize medical research and drug discovery by allowing researchers to see biological mechanism behaviors in ways never before possible.
  7. Perovskite Solar Cells — This new photovoltaic material offers three improvements over the classic silicon solar cell: it is easier to make, can be used virtually anywhere and, to date, keeps on generating power more efficiently.
  8. Open AI Ecosystem — Shared advances in natural language processing and social awareness algorithms, coupled with an unprecedented availability of data, will soon allow smart digital assistants to help with a vast range of tasks, from keeping track of one’s finances and health to advising on wardrobe choice.
  9. Optogenetics — Recent developments mean light can now be delivered deeper into brain tissue, something that could lead to better treatment for people with brain disorders.
  10. Systems Metabolic Engineering — Advances in synthetic biology, systems biology, and evolutionary engineering mean that the list of building block chemicals that can be manufactured better and more cheaply by using plants rather than fossil fuels is growing every year.

To compile this list, the World Economic Forum’s Meta-Council on Emerging Technologies, a panel of global experts, “drew on the collective expertise of the Forum’s communities to identify the most important recent technological trends. By doing so, the Meta-Council aims to raise awareness of their potential and contribute to closing gaps in investment, regulation and public understanding that so often thwart progress.”

You can read 10 expert views on these technologies here or download the series as a PDF.

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How to convert graphene into a semiconductor for scalable production

Progressively magnified images (left to right; scale bars: 400, 10, and 1 nm) of graphene nanoribbons grown on germanium semiconductor wafers.  (credit: Michael Arnold/University of Wisconsin-Madison)

Graphene can be transformed in the lab from a semimetal into a semiconductor if it is confined into nanoribbons narrower than 10 nm (with controlled orientation and edges), but scaling it up for commercial use has not been possible. Until now.

University of Wisconsin-Madison scientists have discovered how to synthesize narrow, long “one-dimensional” (1-D) nanoribbons (sub-10 nanometers wide) directly on a conventional germanium semiconductor wafer.

That narrow width is not possible with the optical and electron-beam lithography techniques conventionally used in making chips, and integrating graphene nanoribbons onto insulating or semiconducting wafers has also been difficult.

The breakthrough was extremely slow growth (under 5 nanometers per hour), using a new variation of a technique called chemical vapor deposition (CVD), allowing nanoribbons with length-to-width aspect ratios greater than 70 to grow on the surface of a germanium wafer (and with the required smooth “armchair” edges — see the image on the right above).

In addition, this new fabrication process is compatible with existing semiconductor fabrication infrastructure. Appears promising. Let’s see which chipmakers go for it.

The research is described in an open-access article just published in Nature Communications.

Abstract of Direct oriented growth of armchair graphene nanoribbons on germanium

Graphene can be transformed from a semimetal into a semiconductor if it is confined into nanoribbons narrower than 10 nm with controlled crystallographic orientation and well-defined armchair edges. However, the scalable synthesis of nanoribbons with this precision directly on insulating or semiconducting substrates has not been possible. Here we demonstrate the synthesis of graphene nanoribbons on Ge(001) via chemical vapour deposition. The nanoribbons are self-aligning 3° from the Geleft fence110right fence directions, are self-defining with predominantly smooth armchair edges, and have tunable width to <10 nm and aspect ratio to >70. In order to realize highly anisotropic ribbons, it is critical to operate in a regime in which the growth rate in the width direction is especially slow, <5 nm h−1. This directional and anisotropic growth enables nanoribbon fabrication directly on conventional semiconductor wafer platforms and, therefore, promises to allow the integration of nanoribbons into future hybrid integrated circuits.

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China’s Sunway TaihuLight tops world supercomputer ratings

Sunway TaihuLight System (credit: National Supercomputing Center)

Chinese supercomputers maintained their No. 1 ranking on the 47th edition of the TOP500 list of the world’s top supercomputers, announced today (June 20). The new Sunway TaihuLight supercomputer operates at 93 petaflop/s (quadrillions of calculations per second) Rmax on the LINPACK benchmark — twice as fast and three times as efficient as China’s Tianhe-2 (at 33.86 petaflop/s), now in the #2 spot.

The new supercomputer was developed by the National Research Center of Parallel Computer Engineering & Technology (NRCPC) and installed at the National Supercomputing Center in Wuxi in China’s Jiangsu province. The complete system has a theoretical peak performance of 125.4 Pflop/s, with 10,649,600 cores and 1.31 PB of primary memory, according to a report by Top500 co-compiler Jack Dongarra of the University of Tennessee.

The newest edition of the semiannual TOP500 list was announced today at the 2016 International Supercomputer Conference in Frankfurt.

China now leads with largest number of supercomputers

The latest list marks the first time since the inception of the TOP500 that the U.S is not home to the largest number of systems. China now leads with 167 systems and the U.S. is second with 165. China also leads the performance category, thanks to the No. 1 and No. 2 systems. Titan, a Cray XK7 system installed at the Department of Energy’s (DOE) Oak Ridge National Laboratory, is now the No. 3 system, at 17.59 petaflop/s.

Sunway TaihuLight was also built entirely using processors designed and made in China (Tianhe-2 was built with Intel processors).

U.S. primacy on the Top500 list has slipped for a number of reasons, including lower government support, private-sector investing now focused on cloud-computing centers, and the U.S. policy of blocking the sale of a number of advanced microprocessors to China, possibly accelerating development of China’s own technology, the New York Times reports. (Last year, the Obama administration began a new effort to develop an“exascale” supercomputer; it would be more than 10 times faster than the Sunway TaihuLight.)

However, because of funding shortages and technology challenges, “there has been a delay in getting the exascale launched in the U.S., and as a result, we’re further behind than we should be,” Dongarra told the Times, noting that the Chinese government is committed to reaching the exascale goal by the end of this decade.

Cray continues to be the leader in the TOP500 list in total installed performance share, with 19.9 percent (down from 25 percent). Thanks to the Sunway TaihuLight system, the National Research Center of Parallel Computer Engineering & Technology takes the second spot with 16.4 percent of the total performance — with just one machine. IBM takes the third spot with 10.7 percent share, down from 14.9 percent six months ago.

Energy-efficiency ratings

For the first time, the data collection and curation of the Green500 project, which ranks supercomputers by energy efficiency, is now integrated with the TOP500 project. The most energy-efficient system and No. 1 on the Green500 is Shoubu, a PEZY Computing/Exascaler ZettaScaler-1.6 System achieving  6.67 GFfops/Watt at the Advanced Center for Computing and Communication at RIKEN in Japan.

Other highlights from the Top 500 list:

  • Total combined performance of all 500 systems has grown to 566.7 petaflop/s, compared to 420 petaflop/s six months ago and 363 petaflop/s one year ago.
  • There are 95 systems with performance greater than a petaflop/s on the list, up from 81 six months ago.
  • Intel continues to provide the processors for the largest share – 455 systems or 91 percent – of the TOP500 systems. The share of IBM Power processors is now at 23 systems, down from 26 systems six month ago. The AMD Opteron family is used in 13 systems (2.6 percent), down from 4.2 percent on the previous list.