Category Archives: electronics

Brainy and brainy: a novel synaptic architecture and a neuromorphic computing platform called SpiNNaker

I have two items about brainlike computing. The first item hearkens back to memristors, a topic I have been following since 2008. (If you’re curious about the various twists and turns just enter  the term ‘memristor’ in this blog’s search engine.) The latest on memristors is from a team than includes IBM (US), École Politechnique Fédérale de Lausanne (EPFL; Swizterland), and the New Jersey Institute of Technology (NJIT; US). The second bit comes from a Jülich Research Centre team in Germany and concerns an approach to brain-like computing that does not include memristors.

Multi-memristive synapses

In the inexorable march to make computers function more like human brains (neuromorphic engineering/computing), an international team has announced its latest results in a July 10, 2018 news item on Nanowerk,

Two New Jersey Institute of Technology (NJIT) researchers, working with collaborators from the IBM Research Zurich Laboratory and the École Polytechnique Fédérale de Lausanne, have demonstrated a novel synaptic architecture that could lead to a new class of information processing systems inspired by the brain.

The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. …

A July 10, 2018 NJIT news release (also on EurekAlert) by Tracey Regan, which originated by the news item, adds more details,

The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications.

Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks – mathematical models of the neurons and synapses of the brain – that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.

However, the implementation of these brain-inspired algorithms on conventional computers is highly inefficient, consuming huge amounts of power and time. This has prompted engineers to search for new materials and devices to build special-purpose computers that can incorporate the algorithms. Nanoscale memristive devices, electrical components whose conductivity depends approximately on prior signaling activity, can be used to represent the synaptic strength between the neurons in artificial neural networks.

While memristive devices could potentially lead to faster and more power-efficient computing systems, they are also plagued by several reliability issues that are common to nanoscale devices. Their efficiency stems from their ability to be programmed in an analog manner to store multiple bits of information; however, their electrical conductivities vary in a non-deterministic and non-linear fashion.

In the experiment, the team showed how multiple nanoscale memristive devices exhibiting these characteristics could nonetheless be configured to efficiently implement artificial intelligence algorithms such as deep learning. Prototype chips from IBM containing more than one million nanoscale phase-change memristive devices were used to implement a neural network for the detection of hidden patterns and correlations in time-varying signals.

“In this work, we proposed and experimentally demonstrated a scheme to obtain high learning efficiencies with nanoscale memristive devices for implementing learning algorithms,” Nandakumar says. “The central idea in our demonstration was to use several memristive devices in parallel to represent the strength of a synapse of a neural network, but only chose one of them to be updated at each step based on the neuronal activity.”

Here’s a link to and a citation for the paper,

Neuromorphic computing with multi-memristive synapses by Irem Boybat, Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Thomas Parnell, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian, & Evangelos Eleftheriou. Nature Communications volume 9, Article number: 2514 (2018) DOI: Published 28 June 2018

This is an open access paper.

Also they’ve got a couple of very nice introductory paragraphs which I’m including here, (from the June 28, 2018 paper in Nature Communications; Note: Links have been removed),

The human brain with less than 20 W of power consumption offers a processing capability that exceeds the petaflops mark, and thus outperforms state-of-the-art supercomputers by several orders of magnitude in terms of energy efficiency and volume. Building ultra-low-power cognitive computing systems inspired by the operating principles of the brain is a promising avenue towards achieving such efficiency. Recently, deep learning has revolutionized the field of machine learning by providing human-like performance in areas, such as computer vision, speech recognition, and complex strategic games1. However, current hardware implementations of deep neural networks are still far from competing with biological neural systems in terms of real-time information-processing capabilities with comparable energy consumption.

One of the reasons for this inefficiency is that most neural networks are implemented on computing systems based on the conventional von Neumann architecture with separate memory and processing units. There are a few attempts to build custom neuromorphic hardware that is optimized to implement neural algorithms2,3,4,5. However, as these custom systems are typically based on conventional silicon complementary metal oxide semiconductor (CMOS) circuitry, the area efficiency of such hardware implementations will remain relatively low, especially if in situ learning and non-volatile synaptic behavior have to be incorporated. Recently, a new class of nanoscale devices has shown promise for realizing the synaptic dynamics in a compact and power-efficient manner. These memristive devices store information in their resistance/conductance states and exhibit conductivity modulation based on the programming history6,7,8,9. The central idea in building cognitive hardware based on memristive devices is to store the synaptic weights as their conductance states and to perform the associated computational tasks in place.

The two essential synaptic attributes that need to be emulated by memristive devices are the synaptic efficacy and plasticity. …

It gets more complicated from there.

Now onto the next bit.


At a guess, those capitalized N’s are meant to indicate ‘neural networks’. As best I can determine, SpiNNaker is not based on the memristor. Moving on, a July 11, 2018 news item on announces work from a team examining how neuromorphic hardware and neuromorphic software work together,

A computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers. The aim is to advance our knowledge of neural processing in the brain, to include learning and disorders such as epilepsy and Alzheimer’s disease.

A July 11, 2018 Frontiers Publishing news release on EurekAlert, which originated the news item, expands on the latest work,

“SpiNNaker can support detailed biological models of the cortex–the outer layer of the brain that receives and processes information from the senses–delivering results very similar to those from an equivalent supercomputer software simulation,” says Dr. Sacha van Albada, lead author of this study and leader of the Theoretical Neuroanatomy group at the Jülich Research Centre, Germany. “The ability to run large-scale detailed neural networks quickly and at low power consumption will advance robotics research and facilitate studies on learning and brain disorders.”

The human brain is extremely complex, comprising 100 billion interconnected brain cells. We understand how individual neurons and their components behave and communicate with each other and on the larger scale, which areas of the brain are used for sensory perception, action and cognition. However, we know less about the translation of neural activity into behavior, such as turning thought into muscle movement.

Supercomputer software has helped by simulating the exchange of signals between neurons, but even the best software run on the fastest supercomputers to date can only simulate 1% of the human brain.

“It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time are currently out of reach.” explains Professor Markus Diesmann, co-author, head of the Computational and Systems Neuroscience department at the Jülich Research Centre.

He continues, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

Developed over the past 15 years and based on the structure and function of the human brain, SpiNNaker — part of the Neuromorphic Computing Platform of the Human Brain Project — is a custom-built computer composed of half a million of simple computing elements controlled by its own software. The researchers compared the accuracy, speed and energy efficiency of SpiNNaker with that of NEST–a specialist supercomputer software currently in use for brain neuron-signaling research.

“The simulations run on NEST and SpiNNaker showed very similar results,” reports Steve Furber, co-author and Professor of Computer Engineering at the University of Manchester, UK. “This is the first time such a detailed simulation of the cortex has been run on SpiNNaker, or on any neuromorphic platform. SpiNNaker comprises 600 circuit boards incorporating over 500,000 small processors in total. The simulation described in this study used just six boards–1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”

Van Albada shares her future aspirations for SpiNNaker, “We hope for increasingly large real-time simulations with these neuromorphic computing systems. In the Human Brain Project, we already work with neuroroboticists who hope to use them for robotic control.”

Before getting to the link and citation for the paper, here’s a description of SpiNNaker’s hardware from the ‘Spiking neural netowrk’ Wikipedia entry, Note: Links have been removed,

Neurogrid, built at Stanford University, is a board that can simulate spiking neural networks directly in hardware. SpiNNaker (Spiking Neural Network Architecture) [emphasis mine], designed at the University of Manchester, uses ARM processors as the building blocks of a massively parallel computing platform based on a six-layer thalamocortical model.[5]

Now for the link and citation,

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model by
Sacha J. van Albada, Andrew G. Rowley, Johanna Senk, Michael Hopkins, Maximilian Schmidt, Alan B. Stokes, David R. Lester, Markus Diesmann, and Steve B. Furber. Neurosci. 12:291. doi: 10.3389/fnins.2018.00291 Published: 23 May 2018

As noted earlier, this is an open access paper.

Neurons and graphene carpets

I don’t entirely grasp the carpet analogy. Actually, I have no why they used a carpet analogy but here’s the June 12, 2018 ScienceDaily news item about the research,

A work led by SISSA [Scuola Internazionale Superiore di Studi Avanzati] and published on Nature Nanotechnology reports for the first time experimentally the phenomenon of ion ‘trapping’ by graphene carpets and its effect on the communication between neurons. The researchers have observed an increase in the activity of nerve cells grown on a single layer of graphene. Combining theoretical and experimental approaches they have shown that the phenomenon is due to the ability of the material to ‘trap’ several ions present in the surrounding environment on its surface, modulating its composition. Graphene is the thinnest bi-dimensional material available today, characterised by incredible properties of conductivity, flexibility and transparency. Although there are great expectations for its applications in the biomedical field, only very few works have analysed its interactions with neuronal tissue.

A June 12, 2018 SISSA press release (also on EurekAlert), which originated the news item, provides more detail,

A study conducted by SISSA – Scuola Internazionale Superiore di Studi Avanzati, in association with the University of Antwerp (Belgium), the University of Trieste and the Institute of Science and Technology of Barcelona (Spain), has analysed the behaviour of neurons grown on a single layer of graphene, observing a strengthening in their activity. Through theoretical and experimental approaches the researchers have shown that such behaviour is due to reduced ion mobility, in particular of potassium, to the neuron-graphene interface. This phenomenon is commonly called ‘ion trapping’, already known at theoretical level, but observed experimentally for the first time only now. “It is as if graphene behaves as an ultra-thin magnet on whose surface some of the potassium ions present in the extra cellular solution between the cells and the graphene remain trapped. It is this small variation that determines the increase in neuronal excitability” comments Denis Scaini, researcher at SISSA who has led the research alongside Laura Ballerini.

The study has also shown that this strengthening occurs when the graphene itself is supported by an insulator, like glass, or suspended in solution, while it disappears when lying on a conductor. “Graphene is a highly conductive material which could potentially be used to coat any surface. Understanding how its behaviour varies according to the substratum on which it is laid is essential for its future applications, above all in the neurological field” continues Scaini, “considering the unique properties of graphene it is natural to think for example about the development of innovative electrodes of cerebral stimulation or visual devices”.

It is a study with a double outcome. Laura Ballerini comments as follows: “This ‘ion trap’ effect was described only in theory. Studying the impact of the ‘technology of materials’ on biological systems, we have documented a mechanism to regulate membrane excitability, but at the same time we have also experimentally described a property of the material through the biology of neurons.”

Dexter Johnson in a June 13, 2018 posting, on his Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website), provides more context for the work (Note: Links have been removed),

While graphene has been tapped to deliver on everything from electronics to optoelectronics, it’s a bit harder to picture how it may offer a key tool for addressing neurological damage and disorders. But that’s exactly what researchers have been looking at lately because of the wonder material’s conductivity and transparency.

In the most recent development, a team from Europe has offered a deeper understanding of how graphene can be combined with neurological tissue and, in so doing, may have not only given us an additional tool for neurological medicine, but also provided a tool for gaining insights into other biological processes.

“The results demonstrate that, depending on how the interface with [single-layer graphene] is engineered, the material may tune neuronal activities by altering the ion mobility, in particular potassium, at the cell/substrate interface,” said Laura Ballerini, a researcher in neurons and nanomaterials at SISSA.

Ballerini provided some context for this most recent development by explaining that graphene-based nanomaterials have come to represent potential tools in neurology and neurosurgery.

“These materials are increasingly engineered as components of a variety of applications such as biosensors, interfaces, or drug-delivery platforms,” said Ballerini. “In particular, in neural electrode or interfaces, a precise requirement is the stable device/neuronal electrical coupling, which requires governing the interactions between the electrode surface and the cell membrane.”

This neuro-electrode hybrid is at the core of numerous studies, she explained, and graphene, thanks to its electrical properties, transparency, and flexibility represents an ideal material candidate.

In all of this work, the real challenge has been to investigate the ability of a single atomic layer to tune neuronal excitability and to demonstrate unequivocally that graphene selectively modifies membrane-associated neuronal functions.

I encourage you to read Dexter’s posting as it clarifies the work described in the SISSA press release for those of us (me) who may fail to grasp the implications.

Here’s a link to and a citation for the paper,

Single-layer graphene modulates neuronal communication and augments membrane ion currents by Niccolò Paolo Pampaloni, Martin Lottner, Michele Giugliano, Alessia Matruglio, Francesco D’Amico, Maurizio Prato, Josè Antonio Garrido, Laura Ballerini, & Denis Scaini. Nature Nanotechnology (2018) DOI: Published online June 13, 2018

This paper is behind a paywall.

All this brings to mind a prediction made about the Graphene Flagship and the Human Brain Project shortly after the European Commission announced in January 2013 that each project had won funding of 1B Euros to be paid out over a period of 10 years. The prediction was that scientists would work on graphene/human brain research.

Colo(u)r-changing nanolaser inspired by chameleons

Caption: Novel nanolaser leverages the same color-changing mechanism that a chameleon uses to camouflage its skin. Credit: Egor Kamelev Courtesy: Northwestern University

I wish there was some detail included about how those colo(u)rs were achieved in that photograph. Strangely, Northwestern University (Chicago, Illinois, US) is more interested in describing the technology that chameleons have inspired. A June 20, 2018 news item on ScienceDaily announces the research,

As a chameleon shifts its color from turquoise to pink to orange to green, nature’s design principles are at play. Complex nano-mechanics are quietly and effortlessly working to camouflage the lizard’s skin to match its environment.

Inspired by nature, a Northwestern University team has developed a novel nanolaser that changes colors using the same mechanism as chameleons. The work could open the door for advances in flexible optical displays in smartphones and televisions, wearable photonic devices and ultra-sensitive sensors that measure strain.

A June 20, 2018 Northwestern University news release (also on EurekAlert) by Amanda Morris, which originated the news item, expands on the theme,

“Chameleons can easily change their colors by controlling the spacing among the nanocrystals on their skin, which determines the color we observe,” said Teri W. Odom, Charles E. and Emma H. Morrison Professor of Chemistry in Northwestern’s Weinberg College of Arts and Sciences. “This coloring based on surface structure is chemically stable and robust.”

The research was published online yesterday [June 19, 2018] in the journal Nano Letters. Odom, who is the associate director of Northwestern’s International Institute of Nanotechnology, and George C. Schatz, Charles E. and Emma H. Morrison Professor of Chemistry in Weinberg, served as the paper’s co-corresponding authors.

The same way a chameleon controls the spacing of nanocrystals on its skin, the Northwestern team’s laser exploits periodic arrays of metal nanoparticles on a stretchable, polymer matrix. As the matrix either stretches to pull the nanoparticles farther apart or contracts to push them closer together, the wavelength emitted from the laser changes wavelength, which also changes its color.

“Hence, by stretching and releasing the elastomer substrate, we could select the emission color at will,” Odom said.

The resulting laser is robust, tunable, reversible and has a high sensitivity to strain. These properties are critical for applications in responsive optical displays, on-chip photonic circuits and multiplexed optical communication.

Here’s a link to and a citation for the paper,

Stretchable Nanolasing from Hybrid Quadrupole Plasmons by Danqing Wang, Marc R. Bourgeois, Won-Kyu Lee, Ran Li, Dhara Trivedi, Michael P. Knudson, Weijia Wang, George C. Schatz, and Teri W. Odom. Nano Lett., Article ASAP DOI: 10.1021/acs.nanolett.8b01774 Publication Date (Web): June 18, 2018

Copyright © 2018 American Chemical Society

This paper is behind a paywall.

New semiconductor material from pigment produced by fungi?

Chlorociboria Aeruginascens fungus on a tree log. (Image: Oregon State University)

Apparently the pigment derived from the fungi you see in the above picture is used by visual artists and, perhaps soon, will be used by electronics manufacturers. From a June 5, 2018 news item on Nanowerk,

Researchers at Oregon State University are looking at a highly durable organic pigment, used by humans in artwork for hundreds of years, as a promising possibility as a semiconductor material.

Findings suggest it could become a sustainable, low-cost, easily fabricated alternative to silicon in electronic or optoelectronic applications where the high-performance capabilities of silicon aren’t required.

Optoelectronics is technology working with the combined use of light and electronics, such as solar cells, and the pigment being studied is xylindein.

A June 5, 2018 Oregon State University news release by Steve Lundeberg, which originated the news item, expands on the theme,

“Xylindein is pretty, but can it also be useful? How much can we squeeze out of it?” said Oregon State University [OSU] physicist Oksana Ostroverkhova. “It functions as an electronic material but not a great one, but there’s optimism we can make it better.”

Xylindien is secreted by two wood-eating fungi in the Chlorociboria genus. Any wood that’s infected by the fungi is stained a blue-green color, and artisans have prized xylindein-affected wood for centuries.

The pigment is so stable that decorative products made half a millennium ago still exhibit its distinctive hue. It holds up against prolonged exposure to heat, ultraviolet light and electrical stress.

“If we can learn the secret for why those fungi-produced pigments are so stable, we could solve a problem that exists with organic electronics,” Ostroverkhova said. “Also, many organic electronic materials are too expensive to produce, so we’re looking to do something inexpensively in an ecologically friendly way that’s good for the economy.”

With current fabrication techniques, xylindein tends to form non-uniform films with a porous, irregular, “rocky” structure.

“There’s a lot of performance variation,” she said. “You can tinker with it in the lab, but you can’t really make a technologically relevant device out of it on a large scale. But we found a way to make it more easily processed and to get a decent film quality.”

Ostroverkhova and collaborators in OSU’s colleges of Science and Forestry blended xylindein with a transparent, non-conductive polymer, poly(methyl methacrylate), abbreviated to PMMA and sometimes known as acrylic glass. They drop-cast solutions both of pristine xylindein and a xlyindein-PMMA blend onto electrodes on a glass substrate for testing.

They found the non-conducting polymer greatly improved the film structure without a detrimental effect on xylindein’s electrical properties. And the blended films actually showed better photosensitivity.

“Exactly why that happened, and its potential value in solar cells, is something we’ll be investigating in future research,” Ostroverkhova said. “We’ll also look into replacing the polymer with a natural product – something sustainable made from cellulose. We could grow the pigment from the cellulose and be able to make a device that’s all ready to go.

“Xylindein will never beat silicon, but for many applications, it doesn’t need to beat silicon,” she said. “It could work well for depositing onto large, flexible substrates, like for making wearable electronics.”

This research, whose findings were recently published in MRS Advances, represents the first use of a fungus-produced material in a thin-film electrical device.

“And there are a lot more of the materials,” Ostroverkhova said. “This is just first one we’ve explored. It could be the beginning of a whole new class of organic electronic materials.”

Here’s a link to and a citation for the paper,

Fungi-Derived Pigments for Sustainable Organic (Opto)Electronics by Gregory Giesbers, Jonathan Van Schenck, Sarath Vega Gutierrez, Sara Robinson. MRS Advances Published online: 21 May 2018

This paper is behind a paywall.

Golden nanoglue

This starts out as a graphene story before taking an abrupt turn. From a June 5, 2018 news item on Nanowerk,

Graphene has undoubtedly been the most popular research subject of nanotechnology during recent years. Made of pure carbon, this material is in principle easy to manufacture: take ordinary graphite and peel one layer off with Scotch tape. The material thus obtained is two-dimensional, yielding unique properties, different from those in three-dimensional materials.

Graphene, however, lacks one important property, semiconductivity, which complicates its usage in electronics applications. Scientists have therefore started the quest of other two-dimensional materials with this desired property.

Molybdenum disulfide, MoS2 is among the most promising candidates. Like graphene, MoS2 consists of layers, interacting weakly with one another. In addition to being a semiconductor, the semiconducting properties of MoS2 change depending on the number of atomic layers.

A June 5, 2018 University of Oulu press release, which originated the news item,  gives more detail about the work,

For the one or few layer MoS2 to be useful in applications, one must be able to join it to other components. What is thus needed is such a metallic conductor that electric current can easily flow between the conductor and the semiconductor. In the case of MoS2, a promising conductor is provided by nickel, which also has other desired properties from the applications point of view.

However, an international collaboration, led by the Nano and molecular systems research unit at the University of Oulu has recently discovered that nanoparticles made of nickel do not attach to MoS2. One needs gold, which ‘glues’ the conductor and the component together. Says docent Wei Cao of NANOMO: “The synthesis is performed through a sonochemical method.” Sonochemistry is a method where chemical reactions are established using ultrasound. NANOMO scientist Xinying Shi adds: “The semiconductor and metal can be bridged either by the crystallized gold nanoparticles, or by the newly formed MoS2-Au-Ni ternary alloy.”

The nanojunction so established has a very small electrical resistivity. It also preserves the semiconducting and magnetic properties of MoS2. In addition, the new material has desirable properties beyond those of the original constituents. For example, it acts as a photocatalyst, which works much more efficiently than pure MoS2. Manufacturing the golden nanojunction is easy and cheap, which makes the new material attractive from the applications point of view.

Here’s a link to and a citation for the paper,

Metallic Contact between MoS2 and Ni via Au Nanoglue by Xinying Shi, Sergei Posysaev, Marko Huttula, Vladimir Pankratov, Joanna Hoszowska, Jean‐Claude Dousse, Faisal Zeeshan, Yuran Niu, Alexei Zakharov, Taohai Li. Small Volume 14, Issue22 May 29, 2018 1704526 First published online: 24 April 2018

This paper is behind a paywall.

There is a pretty illustration of the ‘golden nanojunctions’,

Golden nanoglue (Courtesy of the University of Oulu)

Wooden supercapacitors: a cellulose nanofibril story

A May 24, 2018 news item on Nanowerk announces a technique for making sustainable electrodes (Note: A link has been removed),

Carbon aerogels are ultralight, conductive materials, which are extensively investigated for applications in supercapacitor electrodes in electrical cars and cell phones. Chinese scientists have now found a way to make these electrodes sustainably. The aerogels can be obtained directly from cellulose nanofibrils, the abundant cell-wall material in wood, finds the study reported in the journal Angewandte Chemie (“Wood-Derived Ultrathin Carbon Nanofiber Aerogels”).

A May 24, 2018 Wiley Publications press release, which originated the news item, explains further,

Supercapacitors are capacitors that can take up and release a very large amount of energy in a very short time. Key requirements for supercapacitor electrodes are a large surface area and conductivity, combined with a simple production method. Another growing issue in supercapacitor production–mainly for smartphone and electric car technologies–is sustainability. However, sustainable and economical production of carbon aerogels as supercapacitor electrode materials is possible, propose Shu-Hong Yu and colleagues from the University of Science and Technology of China, Hefei, China.

Carbon aerogels are ultralight conductive materials with a very large surface area. They can be prepared by two production routes: the first and cheapest starts from mostly phenolic components and produces aerogels with improvable conductivity, while the second route is based on graphene- and carbon-nanotube precursors. The latter method delivers high-performance aerogels but is expensive and non-environmentally friendly. In their search for different precursors, Yu and colleagues have found an abundant, far less expensive, and sustainable source: wood pulp.

Well, not really wood pulp, but its major ingredient, nanocellulose. Plant cell walls are stabilized by fibrous nanocellulose, and this extractable material has very recently stimulated substantial research and technological development. It forms a highly porous, but very stable transparent network, and, with the help of a recent technique–oxidation with a radical scavenger called TEMPO–it forms a microporous hydrogel of highly oriented cellulose nanofibrils with a uniform width and length. As organic aerogels are produced from hydrogels by drying and pyrolysis, the authors attempted pyrolysis of supercritically or freeze-dried nanofibrillated cellulose hydrogel.

As it turns out, the method was not as straightforward as expected because ice crystal formation and insufficient dehydration hampered carbonization, according to the authors. Here, a trick helped. The scientists pyrolyzed the dried gel in the presence of the organic acid catalyst para-toluenesulfonic acid. The catalyst lowered the decomposition temperature and yielded a “mechanically stable and porous three-dimensional nanofibrous network” featuring a “large specific surface area and high electrical conductivity,” the authors reported.

The authors also demonstrated that their wood-derived carbon aerogel worked well as a binder-free electrode for supercapacitor applications. The material displayed electrochemical properties comparable to commercial electrodes. The method is an interesting and innovative way in which to fabricate sustainable materials suitable for use in high-performance electronic devices.

This is the first time I’ve seen work on wood-based nanocellulose from China. Cellulose according to its Wikipedia entry is: ” … the most abundant organic polymer on Earth.” For example, there’s more cellulose in cotton than there is wood. So, I find it interesting that in a country not known for its forests, nanocellulose (in this project anyway) is being derived from wood.

Here’s a link to and a citation for the paper,

Wood‐Derived Ultrathin Carbon Nanofiber Aerogels by Si‐Cheng Li, Bi‐Cheng Hu, Dr. Yan‐Wei Ding, Prof. Hai‐Wei Liang, Chao Li, Dr. Zi‐You Yu, Dr. Zhen‐Yu Wu, Prof. Wen‐Shuai Chen, Prof. Shu‐Hong Yu. Angewandt Chemie First published: 23 April 2018 DOI:

This paper is behind a paywall.

Electrode-filled elastic fiber for wearable electronics and robots

This work comes out of Switzerland. A May 25, 2018 École Polytechnique Fédérale de Lausanne (EPFL) press release (also on EurekAlert) announces their fibers,

EPFL scientists have found a fast and simple way to make super-elastic, multi-material, high-performance fibers. Their fibers have already been used as sensors on robotic fingers and in clothing. This breakthrough method opens the door to new kinds of smart textiles and medical implants.

It’s a whole new way of thinking about sensors. The tiny fibers developed at EPFL are made of elastomer and can incorporate materials like electrodes and nanocomposite polymers. The fibers can detect even the slightest pressure and strain and can withstand deformation of close to 500% before recovering their initial shape. All that makes them perfect for applications in smart clothing and prostheses, and for creating artificial nerves for robots.

The fibers were developed at EPFL’s Laboratory of Photonic Materials and Fiber Devices (FIMAP), headed by Fabien Sorin at the School of Engineering. The scientists came up with a fast and easy method for embedding different kinds of microstructures in super-elastic fibers. For instance, by adding electrodes at strategic locations, they turned the fibers into ultra-sensitive sensors. What’s more, their method can be used to produce hundreds of meters of fiber in a short amount of time. Their research has just been published in Advanced Materials.

Heat, then stretch
To make their fibers, the scientists used a thermal drawing process, which is the standard process for optical-fiber manufacturing. They started by creating a macroscopic preform with the various fiber components arranged in a carefully designed 3D pattern. They then heated the preform and stretched it out, like melted plastic, to make fibers of a few hundreds microns in diameter. And while this process stretched out the pattern of components lengthwise, it also contracted it crosswise, meaning the components’ relative positions stayed the same. The end result was a set of fibers with an extremely complicated microarchitecture and advanced properties.

Until now, thermal drawing could be used to make only rigid fibers. But Sorin and his team used it to make elastic fibers. With the help of a new criterion for selecting materials, they were able to identify some thermoplastic elastomers that have a high viscosity when heated. After the fibers are drawn, they can be stretched and deformed but they always return to their original shape.

Rigid materials like nanocomposite polymers, metals and thermoplastics can be introduced into the fibers, as well as liquid metals that can be easily deformed. “For instance, we can add three strings of electrodes at the top of the fibers and one at the bottom. Different electrodes will come into contact depending on how the pressure is applied to the fibers. This will cause the electrodes to transmit a signal, which can then be read to determine exactly what type of stress the fiber is exposed to – such as compression or shear stress, for example,” says Sorin.

Artificial nerves for robots

Working in association with Professor Dr. Oliver Brock (Robotics and Biology Laboratory, Technical University of Berlin), the scientists integrated their fibers into robotic fingers as artificial nerves. Whenever the fingers touch something, electrodes in the fibers transmit information about the robot’s tactile interaction with its environment. The research team also tested adding their fibers to large-mesh clothing to detect compression and stretching. “Our technology could be used to develop a touch keyboard that’s integrated directly into clothing, for instance” says Sorin.

The researchers see many other potential applications. Especially since the thermal drawing process can be easily tweaked for large-scale production. This is a real plus for the manufacturing sector. The textile sector has already expressed interest in the new technology, and patents have been filed.

There’s a video of the lead researcher discussing the work as he offers some visual aids,

Here’s a link to and a citation for the paper,

Superelastic Multimaterial Electronic and Photonic Fibers and Devices via Thermal Drawing by Yunpeng Qu, Tung Nguyen‐Dang, Alexis Gérald Page, Wei Yan, Tapajyoti Das Gupta, Gelu Marius Rotaru, René M. Rossi, Valentine Dominique Favrod, Nicola Bartolomei, Fabien Sorin. Advanced Materials First published: 25 May 2018

This paper is behind a paywall.

Boron nitride nanotubes

Most of the talk about nanotubes is focused on carbon nanotubes but there are other kinds as a May 21, 2018 Rice University news release (also received via email and on EurekAlert and in a May 21, 2018 news item on ScienceDaily), notes,

Boron nitride nanotubes are primed to become effective building blocks for next-generation composite and polymer materials based on a new discovery at Rice University – and a previous one.

Scientists at known-for-nano Rice have found a way to enhance a unique class of nanotubes using a chemical process pioneered at the university. The Rice lab of chemist Angel Martí took advantage of the Billups-Birch reaction process to enhance boron nitride nanotubes.

The work is described in the American Chemical Society journal ACS Applied Nano Materials.

Boron nitride nanotubes, like their carbon cousins, are rolled sheets of hexagonal arrays. Unlike carbon nanotubes, they’re electrically insulating hybrids made of alternating boron and nitrogen atoms.

Insulating nanotubes that can be functionalized will be a valuable building block for nanoengineering projects, Martí said. “Carbon nanotubes have outstanding properties, but you can only get them in semiconducting or metallic conducting types,” he said. “Boron nitride nanotubes are complementary materials that can fill that gap.”

Until now, these nanotubes have steadfastly resisted functionalization, the “decorating” of structures with chemical additives that allows them to be customized for applications. The very properties that give boron nitride nanotubes strength and stability, especially at high temperatures, also make them hard to modify for their use in the production of advanced materials.

But the Billups-Birch reaction developed by Rice Professor Emeritus of Chemistry Edward Billups, which frees electrons to bind with other atoms, allowed Martí and lead author Carlos de los Reyes to give the electrically inert boron nitride nanotubes a negative charge.

That, in turn, opened them up to functionalization with other small molecules, including aliphatic carbon chains.

“Functionalizing the nanotubes modifies or tunes their properties,” Martí said. “When they’re pristine they are dispersible in water, but once we attach these alkyl chains, they are extremely hydrophobic (water-avoiding). Then, if you put them in very hydrophobic solvents like those with long-chain hydrocarbons, they are more dispersible than their pristine form.

“This allows us to tune the properties of the nanotubes and will make it easier to take the next step toward composites,” he said. “For that, the materials need to be compatible.”

After he discovered the phenomenon, de los Reyes spent months trying to reproduce it reliably. “There was a period where I had to do a reaction every day to achieve reproducibility,” he said. But that turned out to be an advantage, as the process only required about a day from start to finish. “That’s the advantage over other processes to functionalize carbon nanotubes. There are some that are very effective, but they may take a few days.”

The process begins with adding pure ammonia gas to the nanotubes and cooling it to -70 degrees Celsius (-94 degrees Fahrenheit). “When it combines with sodium, lithium or potassium — we use lithium — it creates a sea of electrons,” Martí said. “When the lithium dissolves in the ammonia, it expels the electrons.”

The freed electrons quickly bind with the nanotubes and provide hooks for other molecules. De los Reyes enhanced Billups-Birch when he found that adding the alkyl chains slowly, rather than all at once, improved their ability to bind.

The researchers also discovered the process is reversible. Unlike carbon nanotubes that burn away, boron nitride nanotubes can stand the heat. Placing functionalized boron nitride tubes into a furnace at 600 degrees Celsius (1,112 degrees Fahrenheit) stripped them of the added molecules and returned them to their nearly pristine state.

“We call it defunctionalization,” Martí said. “You can functionalize them for an application and then remove the chemical groups to regain the pristine material. That’s something else the material brings that is a little different.”

The researchers have provided this pretty illustration of boron nitride nanotube,

Caption: Rice University researchers have discovered a way to ‘decorate’ electrically insulating boron nitride nanotubes with functional groups, making them more suitable for use with polymers and composite materials. Credit: Martí Research Group/Rice University

Here’s a link to and a citation for the paper,

Chemical Decoration of Boron Nitride Nanotubes Using the Billups-Birch Reaction: Toward Enhanced Thermostable Reinforced Polymer and Ceramic Nanocomposites by Carlos A. de los Reyes, Kendahl L. Walz Mitra, Ashleigh D. Smith, Sadegh Yazdi, Axel Loredo, Frank J. Frankovsky, Emilie Ringe, Matteo Pasquali, and Angel A. Martí. ACS Appl. Nano Mater., Article ASAP DOI: 10.1021/acsanm.8b00633 Publication Date (Web): May 16, 2018

Copyright © 2018 American Chemical Society

This paper is behind a paywall.

Embedded AI (artificial intelligence) with a variant of a memristor?

I don’t entirely get how ReRAM (resistive random access memory) is a variant of a memristor but I’m assuming Samuel K. Moore knows what he’s writing about since his May 16, 2018 posting is on the Nanoclast blog (hosted by the IEEE [Institute of Electrical and Electronics Engineers]), Note: Links have been removed,

Resistive RAM technology developer Crossbar says it has inked a deal with aerospace chip maker Microsemi allowing the latter to embed Crossbar’s nonvolatile memory on future chips. The move follows selection of Crossbar’s technology by a leading foundry for advanced manufacturing nodes. Crossbar is counting on resistive RAM (ReRAM) to enable artificial intelligence systems whose neural networks are housed within the device rather than in the cloud.

ReRAM is a variant of the memristor, a nonvolatile memory device whose resistance can be set or reset by a pulse of voltage. The variant Crossbar qualified for advanced manufacturing is called a filament device. It’s built within the layers above a chip’s silicon, where the IC’s interconnects go, and it’s made up of three layers: from top to bottom—silver, amorphous silicon, and tungsten. Voltage across the amorphous silicon causes a filament of silver atoms to cross the gap to the tungsten, making the memory cell conductive. Reversing the voltage pushes the silver back into place, cutting off conduction.

“The filament itself is only three to four nanometers wide,” says Sylvain Dubois, vice president of marketing and business development at Crossbar. “So the cell itself will be able to scale below 10-nanometers.” What’s more, the ratio between the current that flows when the device is on to when it is off is 1,000 or higher. …

A May 14, 2018 Crossbar news release describes some of the technical AI challenges,

“The biggest challenge facing engineers for AI today is overcoming the memory speed and power bottleneck in the current architecture to get faster data access while lowering the energy cost,” said Dubois. “By enabling a new, memory-centric non-volatile architecture like ReRAM, the entire trained model or knowledge base can be on-chip, connected directly to the neural network with the potential to achieve massive energy savings and performance improvements, resulting in a greatly improved battery life and a better user experience.”

Crossbar’s May 16, 2018 news release provides more detail about their ‘strategic collaboration’ with Microsemi Products (Note: A link has been removed),

Crossbar Inc., the ReRAM technology leader, announced an agreement with Microsemi Corporation, the largest U.S. commercial supplier of military and aerospace semiconductors, in which Microsemi will license Crossbar’s ReRAM core intellectual property. As part of the agreement, Microsemi and Crossbar will collaborate in the research, development and application of Crossbar’s proprietary ReRAM technology in next generation products from Microsemi that integrate Crossbar’s embedded ReRAM with Microsemi products manufactured at the 1x nm process node.

Military and aerospace, eh?

7nm (nanometre) chip shakeup

From time to time I check out the latest on attempts to shrink computer chips. In my July 11, 2014 posting I noted IBM’s announcement about developing a 7nm computer chip and later in my July 15, 2015 posting I noted IBM’s announcement of a working 7nm chip (from a July 9, 2015 IBM news release , “The breakthrough, accomplished in partnership with GLOBALFOUNDRIES and Samsung at SUNY Polytechnic Institute’s Colleges of Nanoscale Science and Engineering (SUNY Poly CNSE), could result in the ability to place more than 20 billion tiny switches — transistors — on the fingernail-sized chips that power everything from smartphones to spacecraft.”

I’m not sure what happened to the IBM/Global Foundries/Samsung partnership but Global Foundries recently announced that it will no longer be working on 7nm chips. From an August 27, 2018 Global Foundries news release,

GLOBALFOUNDRIES [GF] today announced an important step in its transformation, continuing the trajectory launched with the appointment of Tom Caulfield as CEO earlier this year. In line with the strategic direction Caulfield has articulated, GF is reshaping its technology portfolio to intensify its focus on delivering truly differentiated offerings for clients in high-growth markets.

GF is realigning its leading-edge FinFET roadmap to serve the next wave of clients that will adopt the technology in the coming years. The company will shift development resources to make its 14/12nm FinFET platform more relevant to these clients, delivering a range of innovative IP and features including RF, embedded memory, low power and more. To support this transition, GF is putting its 7nm FinFET program on hold indefinitely [emphasis mine] and restructuring its research and development teams to support its enhanced portfolio initiatives. This will require a workforce reduction, however a significant number of top technologists will be redeployed on 14/12nm FinFET derivatives and other differentiated offerings.

I tried to find a definition for FinFet but the reference to a MOSFET and in-gate transistors was too much incomprehensible information packed into a tight space, see the FinFET Wikipedia entry for more, if you dare.

Getting back to the 7nm chip issue, Samuel K. Moore (I don’t think he’s related to the Moore of Moore’s law) wrote an Aug. 28, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electronics and Electrical Engineers] website) which provides some insight (Note: Links have been removed),

In a major shift in strategy, GlobalFoundries is halting its development of next-generation chipmaking processes. It had planned to move to the so-called 7-nm node, then begin to use extreme-ultraviolet lithography (EUV) to make that process cheaper. From there, it planned to develop even more advanced lithography that would allow for 5- and 3-nanometer nodes. Despite having installed at least one EUV machine at its Fab 8 facility in Malta, N.Y., all those plans are now on indefinite hold, the company announced Monday.

The move leaves only three companies reaching for the highest rungs of the Moore’s Law ladder: Intel, Samsung, and TSMC.

It’s a huge turnabout for GlobalFoundries. …

GlobalFoundries rationale for the move is that there are not enough customers that need bleeding-edge 7-nm processes to make it profitable. “While the leading edge gets most of the headlines, fewer customers can afford the transition to 7 nm and finer geometries,” said Samuel Wang, research vice president at Gartner, in a GlobalFoundries press release.

“The vast majority of today’s fabless [emphasis mine] customers are looking to get more value out of each technology generation to leverage the substantial investments required to design into each technology node,” explained GlobalFoundries CEO Tom Caulfield in a press release. “Essentially, these nodes are transitioning to design platforms serving multiple waves of applications, giving each node greater longevity. This industry dynamic has resulted in fewer fabless clients designing into the outer limits of Moore’s Law. We are shifting our resources and focus by doubling down on our investments in differentiated technologies across our entire portfolio that are most relevant to our clients in growing market segments.”

(The dynamic Caulfield describes is something the U.S. Defense Advanced Research Agency is working to disrupt with its $1.5-billion Electronics Resurgence Initiative. Darpa’s [DARPA] partners are trying to collapse the cost of design and allow older process nodes to keep improving by using 3D technology.)

Fabless manufacturing is where the fabrication is outsourced and the manufacturing company of record is focused on other matters according to the Fabless manufacturing Wikipedia entry.

Roland Moore-Colyer (I don’t think he’s related to Moore of Moore’s law either) has written August 28, 2018 article for which also explores this latest news from Global Foundries (Note: Links have been removed),

EVER PREPPED A SPREAD for a party to then have less than half the people you were expecting show up? That’s probably how GlobalFoundries [sic] feels at the moment.

The chip manufacturer, which was once part of AMD, had a fabrication process geared up for 7-nanometre chips which its customers – including AMD and Qualcomm – were expected to adopt.

But AMD has confirmed that it’s decided to move its 7nm GPU production to TSMC, and Intel is still stuck trying to make chips based on 10nm fabrication.

Arguably, this could mark a stymieing of innovation and cutting-edge designs for chips in the near future. But with processors like AMD’s Threadripper 2990WX overclocked to run at 6GHz across all its 32 cores, in the real-world PC fans have no need to worry about consumer chips running out of puff anytime soon. µ

That’s all folks.

Maybe that’s not all

Steve Blank in a Sept. 10, 2018 posting on the Nanoclast blog (on the IEEE [Institute of Electrical and Electronics Engineers] website) provides some provocative commentary on the Global Foundries announcement (Note: A link has been removed),

For most of our lives, the idea that computers and technology would get better, faster, and cheaper every year was as assured as the sun rising every morning. The story “GlobalFoundries Halts 7-nm Chip Development”  doesn’t sound like the end of that era, but for you and anyone who uses an electronic device, it most certainly is.

Technology innovation is going to take a different direction.

This story just goes on and on

There was a new development according to a Sept. 12, 2018 posting on the Nanoclast blog by, again, Samuel K. Moore (Note Links have been removed),

At an event today [sept. 12, 2018], Apple executives said that the new iPhone Xs and Xs Max will contain the first smartphone processor to be made using 7 nm manufacturing technology, the most advanced process node. Huawei made the same claim, to less fanfare, late last month and it’s unclear who really deserves the accolades. If anybody does, it’s TSMC, which manufactures both chips.

TSMC went into volume production with 7-nm tech in April, and rival Samsung is moving toward commercial 7-nm production later this year or in early 2019. GlobalFoundries recently abandoned its attempts to develop a 7 nm process, reasoning that the multibillion-dollar investment would never pay for itself. And Intel announced delays in its move to its next manufacturing technology, which it calls a 10-nm node but which may be equivalent to others’ 7-nm technology.

There’s a certain ‘soap opera’ quality to this with all the twists and turns.